Machine Learning AZ™: HandsOn Python & R In Data Science
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Created by Kirill Eremenko  Data Scientist
Students: 786116, Price: $94.99
Students: 786116, Price: Paid
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
We will walk you stepbystep into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative subfield of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

Part 1  Data Preprocessing

Part 2  Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3  Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4  Clustering: KMeans, Hierarchical Clustering

Part 5  Association Rule Learning: Apriori, Eclat

Part 6  Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7  Natural Language Processing: Bagofwords model and algorithms for NLP

Part 8  Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

Part 9  Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10  Model Selection & Boosting: kfold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises that are based on reallife examples. So not only will you learn the theory, but you will also get some handson practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Important updates (June 2020):

CODES ALL UP TO DATE

DEEP LEARNING CODED IN TENSORFLOW 2.0

TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
Python for Data Science and Machine Learning Bootcamp
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , ScikitLearn , Machine Learning, Tensorflow , and more!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 463995, Price: $89.99
Students: 463995, Price: Paid
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:
 Programming with Python
 NumPy with Python
 Using pandas Data Frames to solve complex tasks
 Use pandas to handle Excel Files
 Web scraping with python
 Connect Python to SQL
 Use matplotlib and seaborn for data visualizations
 Use plotly for interactive visualizations
 Machine Learning with SciKit Learn, including:
 Linear Regression
 K Nearest Neighbors
 K Means Clustering
 Decision Trees
 Random Forests
 Natural Language Processing
 Neural Nets and Deep Learning
 Support Vector Machines
 and much, much more!
Enroll in the course and become a data scientist today!
The Data Science Course 2021: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
Created by 365 Careers  Creating opportunities for Business & Finance students
Students: 411572, Price: $94.99
Students: 411572, Price: Paid
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programmingoriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.

Understanding of the data science field and the type of analysis carried out

Mathematics

Statistics

Python

Applying advanced statistical techniques in Python

Data Visualization

Machine Learning

Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, timeefficient, and structured data science training available online, we created The Data Science Course 2021.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it?
As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a generalpurpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikitlearn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to nontechnical decision makers.
6. Advanced Statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
***What you get***

A $1250 data science training program

Active Q&A support

All the knowledge to get hired as a data scientist

A community of data science learners

A certificate of completion

Access to future updates

Solve reallife business cases that will get you the job
You will become a data scientist from scratch
We are happy to offer an unconditional 30day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a nobrainer for us, as we are certain you will love it.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and become a part of our data scientist program today.
Deep Learning AZ™: HandsOn Artificial Neural Networks
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
Created by Kirill Eremenko  Data Scientist
Students: 309032, Price: $89.99
Students: 309032, Price: Paid
*** As seen on Kickstarter ***
Artificial intelligence is growing exponentially. There is no doubt about that. Selfdriving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go  a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
 Why Deep Learning AZ? 
Here are five reasons we think Deep Learning AZ™ really is different, and stands out from the crowd of other training programs out there:
1. ROBUST STRUCTURE
The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.
That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.
2. INTUITION TUTORIALS
So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.
With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the handson coding exercises you will see for yourself how much more meaningful your experience will be. This is a gamechanger.
3. EXCITING PROJECTS
Are you tired of courses based on overused, outdated data sets?
Yes? Well then you're in for a treat.
Inside this class we will work on RealWorld datasets, to solve RealWorld business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six realworld challenges:
 Artificial Neural Networks to solve a Customer Churn problem
 Convolutional Neural Networks for Image Recognition
 Recurrent Neural Networks to predict Stock Prices
 SelfOrganizing Maps to investigate Fraud
 Boltzmann Machines to create a Recomender System
 Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize
*Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.
4. HANDSON CODING
In Deep Learning AZ™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.
In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain stepbystep where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.
This is a course which naturally extends into your career.
5. INCOURSE SUPPORT
Have you ever taken a course or read a book where you have questions but cannot reach the author?
Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help.
In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum.
No matter how complex your query, we will be there. The bottom line is we want you to succeed.
 The Tools 
Tensorflow and Pytorch are the two most popular opensource libraries for Deep Learning. In this course you will learn both!
TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.
PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.
So which is better and for what?
Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.
The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques.
 More Tools 
Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it.
Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing.
 Even More Tools 
Scikitlearn the most practical Machine Learning library. We will mainly use it:
 to evaluate the performance of our models with the most relevant technique, kFold Cross Validation
 to improve our models with effective Parameter Tuning
 to preprocess our data, so that our models can learn in the best conditions
And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable handson practical coding experience.
Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.
 Who Is This Course For? 
As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning AZ™ your skills are on the cutting edge of today's technology.
If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning AZ™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning AZ™ you will master some of the most cuttingedge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable handson experience with realworld business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.
 RealWorld Case Studies 
Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to realworld scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges:
#1 Churn Modelling Problem
In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.
Your goal is to make an Artificial Neural Network that can predict, based on geodemographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.
If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.
#2 Image Recognition
In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it  by simply changing the pictures in the input folder.
For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog!
#3 Stock Price Prediction
In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have longterm memory, just like us, humans.
The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cuttingedge deep learning methods in our course!
In this part you will learn how to implement this ultrapowerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.
#4 Fraud Detection
According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.
This is the first part of Volume 2  Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.
This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.
#5 & 6 Recommender Systems
From Amazon product suggestions to Netflix movie recommendations  good recommender systems are very valuable in today's World. And specialists who can create them are some of the toppaid Data Scientists on the planet.
We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”.
Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models.
Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.
And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!
 Summary 
In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and realWorld case studies.
We are super enthusiastic about Deep Learning and hope to see you inside the class!
Kirill & Hadelin
Machine Learning & Deep Learning in Python & R
Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 289470, Price: $29.99
Students: 289470, Price: Paid
You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?
You've found the right Machine Learning course!
After completing this course you will be able to:
· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy
· Answer Machine Learning, Deep Learning, R, Python related interview questions
· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.
Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some preprocessing on it. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. We have an indepth knowledge on Machine Learning and Deep Learning techniques using data science and data analysis tools R, Python.
We are also the creators of some of the most popular online courses  with over 600,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. We aim at providing best quality training on data science, machine learning, deep learning using R and Python through this machine learning course.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on data science, machine learning, deep learning using R and Python. Each section contains a practice assignment for you to practically implement your learning on data science, machine learning, deep learning using R and Python.
Table of Contents

Section 1  Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Python basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

Section 2  R basic
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Similar to Python basics, R basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning.

Section 3  Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation. This part of the course is instrumental in gaining knowledge data science, machine learning and deep learning in the later part of the course.

Section 4  Introduction to Machine Learning
In this section we will learn  What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

Section 5  Data Preprocessing
In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

Section 6  Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.

Section 7  Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and KNearest Neighbors.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, testtrain split and how do we finally interpret the result to find out the answer to a business problem.

Section 8  Decision trees
In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R

Section 9  Ensemble technique
In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost.

Section 10  Support Vector Machines
SVM's are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines.

Section 11  ANN Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Section 12  Creating ANN model in Python and R
In this part you will learn how to create ANN models in Python and R.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.

Section 13  CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how grayscale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

Section 14  Creating CNN model in Python and R
In this part you will learn how to create CNN models in Python and R.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 910% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

Section 15  EndtoEnd Image Recognition project in Python and R
In this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

Section 16  Preprocessing Time Series Data
In this section, you will learn how to visualize time series, perform feature engineering, do resampling of data, and various other tools to analyze and prepare the data for models

Section 17  Time Series Forecasting
In this section, you will learn common time series models such as Autoregression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX.
By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You'll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems.
Below is a list of popular FAQs of students who want to start their Machine learning journey
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Why use Python for Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
Why use R for Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastestgrowing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Tableau 2020 AZ: HandsOn Tableau Training for Data Science
Learn Tableau 2020 for data science step by step. Reallife data analytics exercises & quizzes included. Learn by doing!
Created by Kirill Eremenko  Data Scientist
Students: 252507, Price: $99.99
Students: 252507, Price: Paid
Learn data visualization through Tableau 2020 and create opportunities for you or key decisionmakers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks.
You'll learn all of the features in Tableau that allow you to explore, experiment with, fix, prepare, and present data easily, quickly, and beautifully.
Use Tableau to Analyze and Visualize Data So You Can Respond Accordingly

Connect Tableau to a Variety of Datasets

Analyze, Blend, Join, and Calculate Data

Visualize Data in the Form of Various Charts, Plots, and Maps
Convert Raw Data Into Compelling Data Visualizations Using Tableau 2020
Because every module of this course is independent, you can start in whatever section you wish, and you can do as much or as little as you like.
Each section provides a new data set and exercises that will challenge you so you can learn by immediately applying what you're learning.
Content is updated as new versions of Tableau are released. You can always return to the course to further hone your skills, while you stay ahead of the competition.
Contents and Overview
This course begins with Tableau basics. You will navigate the software, connect it to a data file, and export a worksheet, so even beginners will feel completely at ease.
To be able to find trends in your data and make accurate forecasts, you'll learn how to work with data extracts and timeseries.
Also, to make data easier to digest, you'll tackle how to use aggregations to summarize information. You will also use granularity to ensure accurate calculations.
In order to begin visualizing data, you'll cover how to create various charts, maps, scatterplots, and interactive dashboards for each of your projects.
You'll even learn when it's best to join or blend data in order to work with and present information from multiple sources.
Finally, you'll cover the latest and most advanced features of data preparation in Tableau 10, where you will create table calculations, treemap charts, and storylines.
By the time you complete this course, you'll be a highly proficient Tableau user. You will be using your skills as a data scientist to extract knowledge from data so you can analyze and visualize complex questions with ease.
You'll be fully prepared to collect, examine, and present data for any purpose, whether you're working with scientific data or you want to make forecasts about buying trends to increase profits.
R Programming AZ™: R For Data Science With Real Exercises!
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2
Created by Kirill Eremenko  Data Scientist
Students: 210717, Price: $94.99
Students: 210717, Price: Paid
Learn R Programming by doing!
There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly stepbystep. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with reallife analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can't wait to see you in class,
Sincerely,
Kirill Eremenko
Complete Machine Learning with R Studio – ML for 2021
Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language  R studio
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 203884, Price: $29.99
Students: 203884, Price: Paid
You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right?
You've found the right Machine Learning course!
After completing this course, you will be able to:
· Confidently build predictive Machine Learning models using R to solve business problems and create business strategy
· Answer Machine Learning related interview questions
· Participate and perform in online Data Analytics competitions such as Kaggle competitions
Check out the table of contents below to see what all Machine Learning models you are going to learn.
How will this course help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning, R and predictive modelling in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, R and predictive modelling.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an indepth understanding of machine learning and predictive modelling techniques using R.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some preprocessing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques using R, Python, and we have used our experience to include the practical aspects of data analysis in this course.
We are also the creators of some of the most popular online courses  with over 150,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, machine learning, R, predictive modelling, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts of machine learning, R and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R and predictive modelling.
Below is a list of popular FAQs of students who want to start their Machine learning journey
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 3 parts:
Statistics and Probability  Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning  Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience  A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models  Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use R for Machine Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. As compared to Python, R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, usage of R and Python has exploded with it, becoming one of the fastestgrowing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Like Python, adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What are the major advantages of using R over Python?

As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all features that analysts need, R triumphs over Python.

R is a functionbased language, whereas Python is objectoriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python.

R has more data analysis functionality builtin than Python, whereas Python relies on Packages

Python has main packages for data analysis tasks, R has a larger ecosystem of small packages

Graphics capabilities are generally considered better in R than in Python

R has more statistical support in general than Python
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Data Science AZ™: RealLife Data Science Exercises Included
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Created by Kirill Eremenko  Data Scientist
Students: 191699, Price: $89.99
Students: 191699, Price: Paid
Extremely HandsOn... Incredibly Practical... Unbelievably Real!
This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.
In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities  you name it!
This course will give you a full overview of the Data Science journey. Upon completing this course you will know:
 How to clean and prepare your data for analysis
 How to perform basic visualisation of your data
 How to model your data
 How to curvefit your data
 And finally, how to present your findings and wow the audience
This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:
 SQL
 SSIS
 Tableau
 Gretl
This course has preplanned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.
Or you can do the whole course and set yourself up for an incredible career in Data Science.
The choice is yours. Join the class and start learning today!
See you inside,
Sincerely,
Kirill Eremenko
R Basics – R Programming Language Introduction
Learn the essentials of R Programming  R Beginner Level!
Created by RTutorials Training  Data Science Education
Students: 181445, Price: Free
Students: 181445, Price: Free
Are you interested in data science?
Do you want to learn R totally from scratch?
Are you looking for an easy step by step approach to get into R?
Do you want to take an easy R course for BEGINNERS?
Well, if your answer is YES to some of these questions, look no further, this course will help you.
I created this course for the total beginner. That means for you: No prior knowledge required! If this is your first computer programming language to use  congratulations, you found your entry level material. If you are new to data science, no problem, you will learn anything you need to to start out with R.
That also means for you: if you are already used to R, you will likely benefit more from an advanced course. I have more than ten intermediate and advanced R courses available on Udemy, which might be more suited towards your needs. Check out the rtutorials instructor profile for more info.
Let’s take a look at the content and how the course is structured:
We will start with installation, the R and RStudio interface, add on packages, how to use the R exercise database and the R help tools.
Then we will learn various ways to import data, first coding steps including basic R functions, functions and loops and we will also take a look at the graphical tools.
The whole course should take approx. 3 to 5 hours, and there are exercises available for you to try out R. You will also get the code I am using for the demos.
Anything is ready for you to enter the world of statistical programming.
What R you waiting for?
Martin
PythonIntroduction to Data Science and Machine learning AZ
Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks
Created by Yassin Marco  Helped over 895 000+ students in 198 countries
Students: 175112, Price: $94.99
Students: 175112, Price: Paid
Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you :
 Are a student and want to improve your programming skills and want to learn new utilities on how to use Python
 Need to learn basics of Data science
 Have to understand basic Data science tools to improve your career
 Simply acquire the skills for personal use
Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects.
The structure of the course
This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.
Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely understand each concept by writing the code yourself.
For who is this course designed
This course is designed for beginner that are interested to have a basic understand of what exactly Data science is and be able to perform it with python programming language. Since this is an introduction to Data science, you don't have to be a specialist to understand the course. Of course having some basic prior python knowledge could be good but it's not mandatory to be able to understand this course. Also, if you are a student and wish to learn more about Data science or you simply want to improve your python programming skills by learning new tools you will definitely enjoy this course. Finally, this course is for any body that is interested to learn more about Data science and how to properly use python to be able to analyze data with different tools.
Why should I take this course
If you want to learn all the basics of Data science and Python this course has all you need. Not only you will have a complete introduction to Data science but you will also be able to practice python programming in the same course. Indeed, this course is created to help you learn new skills as well as improving your current programming skills.
There is no risk involved in taking this course
This course comes with a 100% satisfaction guarantee, this means that if your are not happy with what you have learned, you have 30 days to get a complete refund with no questions asked. Also, if there is any concept that you find complicated or you are just not able to understand, you can directly contact me and it will be my pleasure to support you in your learning.
This means that you can either learn amazing skills that can be very useful in your professional or everyday life or you can simply try the course and if you don't like it for any reason ask for a refund.
You can't lose with this type of offer !!
ENROLL NOW and start learning today :)
Machine Learning From Basics to Advanced
A beginners guide to learn Machine Learning (including Handson projects  From Basic to Advance Level)
Created by EdYoda Digital University  Visit us at www.edyoda.com
Students: 174110, Price: $74.99
Students: 174110, Price: Paid
If you are looking to start your career in Machine learning then this is the course for you.
This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.
This course has 5 parts as given below:

Introduction & Data Wrangling in machine learning

Linear Models, Trees & Preprocessing in machine learning

Model Evaluation, Feature Selection & Pipelining in machine learning

Bayes, Nearest Neighbors & Clustering in machine learning

SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning
For the code explained in each lecture, you can find a GitHub link in the resources section.
Who's teaching you in this course?
I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies  PySpark, Spark using Scala Machine Learning & Deep Learning scikitlearn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan  Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS  Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML.
Machine learning is the fuel we need to power robots, alongside AI. With Machine Learning, we can power programs that can be easily updated and modified to adapt to new environments and tasks to get things done quickly and efficiently.
Here are a few reasons for you to pursue a career in Machine Learning:
1) Machine learning is a skill of the future – Despite the exponential growth in Machine Learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in Machine Learning, you will have a secure career in a technology that is on the rise.
2) Work on real challenges – Businesses in this digital age face a lot of issues that Machine learning promises to solve. As a Machine Learning Engineer, you will work on reallife challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve realworld struggles gives high satisfaction.
3) Learn and grow – Since Machine Learning is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer.
4) An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have the experience and expertise to follow an upward career graph and approach your ideal employers.
5) Build a lucrative career– The average salary of a Machine Learning engineer is one of the top reasons why Machine Learning seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by.
6) Sidestep into data science – Machine learning skills help you expand avenues in your career. Machine Learning skills can endow you with two hats the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge.
Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise.
Enroll Now!! See You in Class.
Happy learning
Team Edyoda
Artificial Intelligence AZ™: Learn How To Build An AI
Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for RealWorld applications!
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 160976, Price: $129.99
Students: 160976, Price: Paid
*** AS SEEN ON KICKSTARTER ***
Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:
 How to start building AI with no previous coding experience using Python
 How to merge AI with OpenAI Gym to learn as effectively as possible
 How to optimize your AI to reach its maximum potential in the real world
Here is what you will get with this course:
1. Complete beginner to expert AI skills – Learn to code selfimproving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.
2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.
3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.
4. Realworld solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.
5. Incourse support – We’re fully committed to making this the most accessible and resultsdriven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum.
Machine Learning, Data Science and Deep Learning with Python
Complete handson machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Created by Sundog Education by Frank Kane  Founder, Sundog Education. Machine Learning Pro
Students: 150695, Price: $89.99
Students: 150695, Price: Paid
New! Updated for 2021 with extra content on generative models: variational autoencoders (VAE's) and generative adversarial models (GAN's)
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the toppaying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money  it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry  and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include handson Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course  the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

Creating synthetic images with Variational AutoEncoders (VAE's) and Generative Adversarial Networks (GAN's)

Data Visualization in Python with MatPlotLib and Seaborn

Transfer Learning

Sentiment analysis

Image recognition and classification

Regression analysis

KMeans Clustering

Principal Component Analysis

Train/Test and cross validation

Bayesian Methods

Decision Trees and Random Forests

Multiple Regression

MultiLevel Models

Support Vector Machines

Reinforcement Learning

Collaborative Filtering

KNearest Neighbor

Bias/Variance Tradeoff

Ensemble Learning

Term Frequency / Inverse Document Frequency

Experimental Design and A/B Tests

Feature Engineering

Hyperparameter Tuning
...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.
If you're new to Python, don't worry  the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windowsbased PC's, Linux desktops, and Macs.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by realworld industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

"I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing."  Kanad Basu, PhD
Artificial Intelligence in Web Design Certification (2021)
MBA in Creative Arts, Design and Animation: Level 2  Part 1 course that teaches Artificial Intelligence (AI) Web Design
Created by Srinidhi Ranganathan  Digital Marketing Legend  India's Top Udemy Instructor
Students: 149870, Price: $24.99
Students: 149870, Price: Paid
Welcome to Level 2 of the series "MBA in Creative Arts, Design and Animation".
What is this 2021 Website Design course all about?
Website design is the art and science of building the look, feel, and how a website functions in a nutshell. This course is having clear, concise, and easy to use website design technologies and will ultimately lead to a better user experience for your custom audience or clients. There are many aspects of successful website design like HTML, colors, layouts, text size, graphics, and so much more. But, this course is a huge differentiator in the design field as it uses artificial intelligencebased website design stateoftheart technologies that are covered nowhere in the world. If you've been wondering how to learn website design, you've come to the right place  after all.
Why website design is needed for a successful online presence?
Having a good website is the backbone of every business and to achieve this successful feat, typically we need website design developers, content marketers, SEO specialists, etc. in an organization. But, some website design developers charge a lot of money outside in the market and other freelancing marketplaces to design websites using content management platforms like WordPress CMS. In fact, tools like Bookmark in website design will help advance your webdesign career, altogether. This course is both a beginner's website design course and an advanced course for web developers.
Jobs in Website Design in 2021:
According to Glassdoor, you can expect an average salary of $64,468 in the United States for doing website design. As your experience grows in website design all the time, you can expect to see higher salary ranges in the upcoming future. For example, you can expect an average Junior Website Designer to make around $62k in the United States alone and Rs. 23 Lakhs in India. As a Front End Website Design Developer, you can expect to make over $90k abroad.
Introduction to the website design course powered by Artificial Intelligence technology:
In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance.
We’ve come a long way from that time. The internet is still a dark, dreadful place, but it’s much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligencebased website creation tools that are dominating the digital marketing industry.
The technology is still in its infancy stage, however  but machine learning is enabling artificial design intelligence (ADI) to understand creative rules and apply them independently and in an intuitive and more attractive way  for that matter. Artificial design technology will soon be advanced enough to automate a lot of web design work in the near future too. New thought leaders would emerge and new courses like this would serve to be gamechangers in the artificial intelligencepowered digital marketing space.
This gamechanging course focuses on "Artificial Intelligence in Web Design Certification" taught by Digital Marketing Legend "Srinidhi Ranganathan" will cover artificial intelligence tools in website, chatbot design, and analytics in website design which will help you to create a website in merely minutes in 2021.
I will teach you to easily create websites in the fastest time possible using advanced website design tools or design techniques and customize your site look and feel according to your requirement in a simple draganddrop timeline by talking to chatbots.
Why learn this artificial intelligence gamechanging course on website design and how is this a differentiator?
This website design course can change your life as a web developer or marketer. With no coding experience, you can create amazing looking websites and pave the path for unlimited designs and interchange content and play god using artificial intelligence tech.
This course will save you a ton of time when it comes to creating websites without using any expensive website design tool and without using complex tools like WordPress etc. You do not even need to outsource websites to other agencies ever again as you can do it yourself now in minutes.
About Bookmark  The 2021 Artificial Intelligence Based Website Design Builder
Bookmark is an AIpowered website builder to help you design amazing websites at lightning speed. Bookmarks AI software AiDA (Artificial Intelligence Design Assistant)  The algorithm behind the website design empowers the nontechnical entrepreneur and small business owner with the ability to instantly create an exceptional website that one can be proud of. AiDA eliminates up to 90% of the pain points associated with website design and creation by building a brand new, striking website in less than 30 seconds and then simply walks the user through the process of editing content and design. AiDA's features taught in the course comprises automatically moving the mouse cursor to aid in the website design process and instant change of website design style and fonts in a matter of minutes.
The question is "Are you ready to get into action and embrace the power to leverage artificial intelligence in website design using AiDA?”.
If yes, plunge into action right away by signing up NOW. All the best to become an Artificial Intelligence WebDesign Creator.
R Programming For Absolute Beginners
Learn the basics of writing code in R  your first step to become a data scientist
Created by Bogdan Anastasiei  University Teacher and Consultant
Students: 140637, Price: $39.99
Students: 140637, Price: Paid
If you have decided to learn R as your data science programming language, you have made an excellent decision!
R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist.
The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.
This course contains about 100 video lectures in nine sections.
In the first section of this course you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables.
The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more. These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling.
After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions.
The next section is about working with strings. Here we will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string and especially how to create regular expressions that identify patterns in strings.
In the following section you’ll learn how to build charts in R. We are going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line and boxplot. Moreover, you will learn how to plot a function of one variable and how to export the charts you create.
Every command and function is visually explained: you can see the output live. At the end of each section you will find a PDF file with practical exercises that allow you to apply and strengthen your knowledge.
So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey!
Python AZ™: Python For Data Science With Real Exercises!
Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization
Created by Kirill Eremenko  Data Scientist
Students: 130580, Price: $99.99
Students: 130580, Price: Paid
Learn Python Programming by doing!
There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly stepbystep. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with reallife analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can't wait to see you in class,
Sincerely,
Kirill Eremenko
Introduction to Data Science using Python (Module 1/3)
Learn Data science / Machine Learning using Python (Scikit Learn)
Created by Rakesh Gopalakrishnan  Over 260,000 Students
Students: 120642, Price: Free
Students: 120642, Price: Free
Are you completely new to Data science?
Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?
Do you want to start or switch career to Data Science and analytics?
If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover indepth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and ScikitLearn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.
This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.
The other modules will cover more complex concepts.
Artificial Neural Networks (ANN) with Keras in Python and R
Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 119295, Price: $19.99
Students: 119295, Price: Paid
You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?
You've found the right Neural Networks course!
After completing this course you will be able to:

Identify the business problem which can be solved using Neural network Models.

Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses  with over 250,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:

Part 1  Python and R basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Part 2  Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 3  Creating Regression and Classification ANN model in Python and R
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 4  Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data preprocessing topics like missing value imputation, variable transformation and TestTrain split.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
StartTech Academy

Below are some popular FAQs of students who want to start their Deep learning journey
Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Complete Linear Regression Analysis in Python
Linear Regression in Python Simple Regression, Multiple Regression, Ridge Regression, Lasso and subset selection also
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 117389, Price: $19.99
Students: 117389, Price: Paid
You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right?
You've found the right Linear Regression course!
After completing this course you will be able to:

Identify the business problem which can be solved using linear regression technique of Machine Learning.

Create a linear regression model in Python and analyze its result.

Confidently practice, discuss and understand Machine Learning concepts
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
How this course will help you?
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some preprocessing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses  with over 150,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:

Section 1  Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics
then graphical representations to describe the data and then a lecture on measures of center like mean
median and mode and lastly measures of dispersion like range and standard deviation

Section 2  Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it'll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Section 3  Introduction to Machine Learning
In this section we will learn  What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

Section 4  Data Preprocessing
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

Section 5  Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
StartTech Academy

Below is a list of popular FAQs of students who want to start their Machine learning journey
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
What is the Linear regression technique of Machine learning?
Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression.
When there are multiple input variables, the method is known as multiple linear regression.
Why learn Linear regression technique of Machine learning?
There are four reasons to learn Linear regression technique of Machine learning:
1. Linear Regression is the most popular machine learning technique
2. Linear Regression has fairly good prediction accuracy
3. Linear Regression is simple to implement and easy to interpret
4. It gives you a firm base to start learning other advanced techniques of Machine Learning
How much time does it take to learn Linear regression technique of machine learning?
Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 4 parts:
Statistics and Probability  Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning  Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience  A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of Linear Regression modelling  Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic endtoend and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use Python for data Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Time Series Analysis and Forecasting using Python
Learn about time series analysis & forecasting models in Python Time Data VisualizationARMAARIMARegression ANN
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 108928, Price: $29.99
Students: 108928, Price: Paid
You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?
You've found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.
After completing this course you will be able to:

Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.

Implement multivariate time series forecasting models based on Linear regression and Neural Networks.

Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations
How will this course help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.
If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques.
Why should you choose this course?
We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through howto examples. Each section has the following components:

Theoretical concepts and use cases of different forecasting models, time series forecasting and time series analysis

Stepbystep instructions on implement time series forecasting models in Python

Downloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques

Class notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques
The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques.
.What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an indepth knowledge on time series forecasting, time series analysis and Python time series techniques.
We are also the creators of some of the most popular online courses  with over 170,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques.
Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.
What is covered in this course?
Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models to

See patterns in time series data

Make forecasts based on models
Let me give you a brief overview of the course

Section 1  Introduction
In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.

Section 2  Python basics
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it'll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course.

Section 3  Basics of Time Series Data
In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.

Section 4  Preprocessing Time Series Data
In this section, you will learn how to visualize time series, perform feature engineering, do resampling of data, and various other tools to analyze and prepare the data for models and execute time series forecasting, time series analysis and implement Python time series techniques.

Section 5  Getting Data Ready for Regression Model
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment and missing value imputation.

Section 6  Forecasting using Regression Model
This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.

Section 7  Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Section 8  Creating Regression and Classification ANN model in Python
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.
Go ahead and click the enroll button, and I'll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques!
Cheers
StartTech Academy
Statistics for Data Science and Business Analysis
Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
Created by 365 Careers  Creating opportunities for Business & Finance students
Students: 108459, Price: $94.99
Students: 108459, Price: Paid
Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist?
Well then, you’ve come to the right place!
Statistics for Data Science and Business Analysis is here for you with TEMPLATES in Excel included!
This is where you start. And it is the perfect beginning!
In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to reallife situations. We have created a course that is:

Easy to understand

Comprehensive

Practical

To the point

Packed with plenty of exercises and resources

Datadriven

Introduces you to the statistical scientific lingo

Teaches you about data visualization

Shows you the main pillars of quant research
It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction.
Teaching is our passion
We worked hard for over four months to create the best possible Statistics course which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. Highquality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing.
What makes this course different from the rest of the Statistics courses out there?

Highquality production – HD video and animations (This isn’t a collection of boring lectures!)

Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level)

Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist

Extensive Case Studies that will help you reinforce everything you’ve learned

Excellent support  if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day

Dynamic  we don’t want to waste your time! The instructor sets a very good pace throughout the whole course
Why do you need these skills?

Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow

Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth

Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated

Growth  this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new
Please bear in mind that the course comes with Udemy’s 30day unconditional moneyback guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.
Let's start learning together now!
Docker Course for Beginners
Dive into the world of Docker and learn about Dockerfiles and Container Management
Created by EdYoda Digital University  Visit us at www.edyoda.com
Students: 105842, Price: $29.99
Students: 105842, Price: Paid
Containerization of the applications is going on in the full swing across the IT industry. Docker's course covers the fundamental concepts of Docker containers. Along with the concepts it also covers the most useful commands related to container management, image management, and Dockerfile. After studying this course one would be ready to dive deeper into the world of container orchestration.
Docker's course becomes the necessary prerequisite for learning Docker Swarm and Kubernetes.
Enroll now!! see you in class.
Happy Learning!
Team Edyoda
Convolutional Neural Networks in Python: CNN Computer Vision
Python for Computer Vision & Image Recognition  Deep Learning Convolutional Neural Network (CNN)  Keras & TensorFlow 2
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 97264, Price: $19.99
Students: 97264, Price: Paid
You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?
You've found the right Convolutional Neural Networks course!
After completing this course you will be able to:

Identify the Image Recognition problems which can be solved using CNN Models.

Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts

Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.
If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses  with over 300,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:

Part 1 (Section 2) Python basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Part 2 (Section 36)  ANN Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 3 (Section 711)  Creating ANN model in Python
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 4 (Section 12)  CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how grayscale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

Part 5 (Section 1314)  Creating CNN model in Python
In this part you will learn how to create CNN models in Python.We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 910% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

Part 6 (Section 1518)  EndtoEnd Image Recognition project in Python
In this section we build a complete image recognition project on colored images.We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
StartTech Academy

Below are some popular FAQs of students who want to start their Deep learning journey
Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Python & Machine Learning for Financial Analysis
Master Python Programming Fundamentals and Harness the Power of ML to Solve RealWorld Practical Applications in Finance
Created by Dr. Ryan Ahmed, Ph.D., MBA  Professor & Bestselling Udemy Instructor, 200K+ students
Students: 92935, Price: $99.99
Students: 92935, Price: Paid
Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking?
If the answer is yes, then welcome to the “The Complete Python and Machine Learning for Financial Analysis” course in which you will learn everything you need to develop practical realworld finance/banking applications in Python!
So why Python?
Python is ranked as the number one programming language to learn in 2020, here are 6 reasons you need to learn Python right now!
1. #1 language for AI & Machine Learning: Python is the #1 programming language for machine learning and artificial intelligence.
2. Easy to learn: Python is one of the easiest programming language to learn especially of you have not done any coding in the past.
3. Jobs: high demand and low supply of python developers make it the ideal programming language to learn now.
4. High salary: Average salary of Python programmers in the US is around $116 thousand dollars a year.
5. Scalability: Python is extremely powerful and scalable and therefore realworld apps such as Google, Instagram, YouTube, and Spotify are all built on Python.
6. Versatility: Python is the most versatile programming language in the world, you can use it for data science, financial analysis, machine learning, computer vision, data analysis and visualization, web development, gaming and robotics applications.
This course is unique in many ways:
1. The course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry. A detailed overview is shown below:
a) Part #1 – Python Programming Fundamentals: Beginner’s Python programming fundamentals covering concepts such as: data types, variables assignments, loops, conditional statements, functions, and Files operations. In addition, this section will cover key Python libraries for data science such as Numpy and Pandas. Furthermore, this section covers data visualization tools such as Matplotlib, Seaborn, Plotly, and Bokeh.
b) Part #2 – Financial Analysis in Python: This part covers Python for financial analysis. We will cover key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio. In addition, we will cover Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier. We will also cover trading strategies such as momentumbased and moving average trading.
c) Part #3 – AI/Ml in Finance/Banking: This section covers practical projects on AI/ML applications in Finance. We will cover application of Deep Neural Networks such as Long Short Term Memory (LSTM) networks to perform stock price predictions. In addition, we will cover unsupervised machine learning strategies such as KMeans Clustering and Principal Components Analysis to perform Baking Customer Segmentation or Clustering. Furthermore, we will cover the basics of Natural Language Processing (NLP) and apply it to perform stocks sentiment analysis.
2. There are several mini challenges and exercises throughout the course and you will learn by doing. The course contains mini challenges and coding exercises in almost every video so you will learn in a practical and easy way.
3. The Projectbased learning approach: you will build more than 6 full practical projects that you can add to your portfolio of projects to showcase your future employer during job interviews.
So who is this course for?
This course is geared towards the following:

Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.

Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors.

Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain realworld practical experience.
There is no prior experience required, Even if you have never used python or any programming language before, don’t worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge.
In this course, (1) you will have a true practical projectbased learning experience, we will build more than 6 projects together (2) You will have access to all the codes and slides, (3) You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in python programming to employers. (4) All of this comes with a 30 day money back guarantee so you can give a course a try risk free! Check out the preview videos and the outline to get an idea of the projects we will be covering.
Enroll today and I look forward to seeing you inside!
Complete Guide to TensorFlow for Deep Learning with Python
Learn how to use Google's Deep Learning Framework  TensorFlow with Python! Solve problems with cutting edge techniques!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 89033, Price: $124.99
Students: 89033, Price: Paid
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
 Neural Network Basics
 TensorFlow Basics
 Artificial Neural Networks
 Densely Connected Networks
 Convolutional Neural Networks
 Recurrent Neural Networks
 AutoEncoders
 Reinforcement Learning
 OpenAI Gym
 and much more!
There are many Deep Learning Frameworks out there, so why use TensorFlow?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
Become a machine learning guru today! We'll see you inside the course!
Neural Networks in Python: Deep Learning for Beginners
Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow Python
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 88703, Price: $19.99
Students: 88703, Price: Paid
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?
You've found the right Neural Networks course!
After completing this course you will be able to:

Identify the business problem which can be solved using Neural network Models.

Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses  with over 250,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:

Part 1  Python basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Part 2  Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 3  Creating Regression and Classification ANN model in Python
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 4  Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data preprocessing topics like missing value imputation, variable transformation and TestTrain split.

Part 5  Classic ML technique  Linear Regression
This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
StartTech Academy

Below are some popular FAQs of students who want to start their Deep learning journey
Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
The Top 5 Machine Learning Libraries in Python
A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
Created by Mike West  Creator of LogikBot
Students: 86713, Price: Free
Students: 86713, Price: Free
Recent Review from Similar Course:
"This was one of the most useful classes I have taken in a long time. Very specific, realworld examples. It covered several instances of 'what is happening', 'what it means' and 'how you fix it'. I was impressed." Steve
Welcome to The Top 5 Machine Learning Libraries in Python. This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.
What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.
The top career in the world is the data scientist. Great. What’s a data scientist?
The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.
Business generate a huge amount of data. The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in. The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.
Don’t I need a PhD? Nope. Some data scientists do have PhDs but it’s not a requirement. A similar career to that of the data scientist is the machine learning engineer.
A machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model. They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.
In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.
A library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.
Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course.
Artificial Neural Networks for Business Managers in R Studio
You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work
Created by StartTech Academy  3,000,000+ Enrollments  4+ Rated  160+ Countries
Students: 86577, Price: $19.99
Students: 86577, Price: Paid
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?
You've found the right Neural Networks course!
After completing this course you will be able to:

Identify the business problem which can be solved using Neural network Models.

Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

Create Neural network models in R using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses  with over 250,000 enrollments and thousands of 5star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman  Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price.  Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:

Part 1  Setting up R studio and R Crash course
This part gets you started with R.
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.

Part 2  Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 3  Creating Regression and Classification ANN model in R
In this part you will learn how to create ANN models in R Studio.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 4  Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data preprocessing topics like missing value imputation, variable transformation and TestTrain split.

Part 5  Classic ML technique  Linear Regression
This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
StartTech Academy

Below are some popular FAQs of students who want to start their Deep learning journey
Why use R for Deep Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastestgrowing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decisionmaking, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Power BI AZ: HandsOn Power BI Training For Data Science!
Learn Microsoft Power BI for Data Science and Data Analytics. Build visualizations and BI reports with Power BI Desktop
Created by Kirill Eremenko  Data Scientist
Students: 86535, Price: $109.99
Students: 86535, Price: Paid
Learn data visualization through Microsoft Power BI and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks.
You'll learn all of the features in Power BI that allow you to explore, experiment with, fix, prepare, and present data easily, quickly, and beautifully.
Use Power BI to Analyze and Visualize Data So You Can Respond Accordingly
 Connect Power BI to a Variety of Datasets
 Drill Down and Up in Your Visualization and Calculate Data
 Visualize Data in the Form of Various Charts, Plots, and Maps
Convert Raw Data Into Compelling Data Visualizations Using Power BI
Because every module of this course is independent, you can start in whatever section you wish, and you can do as much or as little as you like.
Each section provides a new data set and exercises that will challenge you so you can learn by immediately applying what you're learning.
Content is updated as new versions of Power BI are released. You can always return to the course to further hone your skills, while you stay ahead of the competition.
Contents and Overview
This course begins with Power BI basics. You will navigate the software, connect it to a data file, and export a worksheet, so even beginners will feel completely at ease.
To be able to find trends in your data and make accurate forecasts, you'll learn how to work with hierarchies and timeseries.
Also, to make data easier to digest, you'll tackle how to use aggregations to summarize information. You will also use granularity to ensure accurate calculations.
In order to begin visualizing data, you'll cover how to create various charts, maps, scatterplots, and interactive dashboards for each of your projects.
You'll even learn how to join multiple data sources into one in order to combine diverse sources of information in one analytical solution.
Finally, you'll cover some of the latest and most advanced custom visualizations in Microsoft Power BI, where you will create histograms, brickcharts and more.
By the time you complete this course, you'll be a highly proficient Power BI user. You will be using your skills as a data scientist to extract knowledge from data so you can analyze and visualize complex questions with ease.
You'll be fully prepared to collect, examine, and present data for any purpose, whether you're working with scientific data or you want to make forecasts about buying trends to increase profits.