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.
Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 241961, Price: $19.99
Students: 241961, Price: Paid
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.
One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.
Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.
This course is designed to remove that obstacle  to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.
So what are those things?
Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.
The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.
That means you can do vector and matrix operations like addition, subtraction, and multiplication.
The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.
Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.
Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.
Pandas makes working with datasets a lot like R, if you’re familiar with R.
The central object in R and Pandas is the DataFrame.
We’ll look at how much easier it is to load a dataset using Pandas vs. trying to do it manually.
Then we’ll look at some dataframe operations useful in machine learning, like filtering by column, filtering by row, and the apply function.
Pandas dataframes will remind you of SQL tables, so if you have an SQL background and you like working with tables then Pandas will be a great next thing to learn about.
Since Pandas teaches us how to load data, the next step will be looking at the data. For that we will use Matplotlib.
In this section we’ll go over some common plots, namely the line chart, scatter plot, and histogram.
We’ll also look at how to show images using Matplotlib.
99% of the time, you’ll be using some form of the above plots.
Scipy.
I like to think of Scipy as an addon library to Numpy.
Whereas Numpy provides basic building blocks, like vectors, matrices, and operations on them, Scipy uses those general building blocks to do specific things.
For example, Scipy can do many common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.
It has signal processing tools so it can do things like convolution and the Fourier transform.
In sum:
If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

you should already know "why" things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses)
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
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.
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!
Concentration and Focus: The Principles of Deep Work
Apply Dr. Cal Newport's Deep Work methodology to Get Stuff Done
Created by Nathan Robertson  EdTech Startup Director, Bilingual, ExActor, Author
Students: 52250, Price: Free
Students: 52250, Price: Free
Everybody wants to get more done, but we go about it the wrong way.
We think getting more done requires more time. We will put in 80 hours of work a week, getting an abysmal return on investment for the last 20  30 hours. We can't elbow grease our way through problems by throwing time at them.
We don't need more time to work. We need to change how we work.
This class, based on Dr. Cal Newport's Deep Work principles, provides the concepts to do just that. Deep Work is all about cutting out the superfluous, unnecessary tasks in our professional and personal life. It will help you focus in on the things you do that provide the most value  to the market, and to your own life.
This is a short primer course with the videos you need to get the basics, and follow up resources if you are curious exploring this deeper.
Don't waste your life working. Work smarter, and provide more value to the world, with Deep Work.
Data Science: Deep Learning and Neural Networks in Python
The MOST indepth look at neural network theory, and how to code one with pure Python and Tensorflow
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 46598, Price: $89.99
Students: 46598, Price: Paid
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build fullon nonlinear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.
We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.
Next, we implement a neural network using Google's new TensorFlow library.
You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks  slightly modified architectures and what they are used for.
NOTE:
If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPUoptimization, check out my followup course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.
I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning and Computer Vision AZ™: OpenCV, SSD & GANs
Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 42215, Price: $89.99
Students: 42215, Price: Paid
*** AS SEEN ON KICKSTARTER ***
You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.
But what if you could also become a creator?
What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?
Sounds too good to be true, doesn't it?
But there actually is a way..
Computer Vision is by far the easiest way of becoming a creator.
And it's not only the easiest way, it's also the branch of AI where there is the most to create.
Why? You'll ask.
That's because Computer Vision is applied everywhere. From health to retail to entertainment  the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.
Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?
And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of.
So now that raises the question: how do you break into the World of Computer Vision?
Up until now, computer vision has for the most part been a maze. A growing maze.
As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost.
On top of that, not only do you need to know how to use it  you also need to know how it works to maximise the advantage of using Computer Vision.
To this problem we want to bring...
Computer Vision AZ.
With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!
Can't wait to see you inside the class,
Kirill & Hadelin
Natural Language Processing with Deep Learning in Python
Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 39935, Price: $39.99
Students: 39935, Price: Paid
In this course we are going to look at NLP (natural language processing) with deep learning.
Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bagofwords and termdocument matrices.
These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.
In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.
First up is word2vec.
In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.
Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

king  man = queen  woman

France  Paris = England  London

December  Novemeber = July  June
For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pretrained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.
We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.
Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.
We will also look at some classical NLP problems, like partsofspeech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.
Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bagofwords.
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

calculus (taking derivatives)

matrix addition, multiplication

probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own

Can write a feedforward neural network in Theano or TensorFlow

Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function

Helpful to have experience with tree algorithms
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Applied Deep Learning: Build a Chatbot – Theory, Application
Understand the Theory of how Chatbots work and implement them in Python and PyTorch!
Created by Fawaz Sammani  Computer Vision Researcher
Students: 36278, Price: Free
Students: 36278, Price: Free
In this course, you'll learn the following:

RNNs and LSTMs

Sequence Modeling

PyTorch

Building a Chatbot in PyTorch
We will first cover the theoretical concepts you need to know for building a Chatbot, which include RNNs, LSTMS and Sequence Models with Attention.
Then we will introduce you to PyTorch, a very powerful and advanced deep learning Library. We will show you how to install it and how to work with it and with PyTorch Tensors.
Then we will build our Chatbot in PyTorch!
Please Note an important thing: If you don't have prior knowledge on Neural Networks and how they work, you won't be able to cope well with this course. Please note that this is not a Deep Learning course, it's an Application of Deep Learning, as the course names implies (Applied Deep Learning: Build a Chatbot). The course level is Intermediate, and not Beginner. So please familiarize yourself with Neural Networks and it's concepts before taking this course. If you are already familiar, then your ready to start this journey!
Python for Computer Vision with OpenCV and Deep Learning
Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 35961, Price: $99.99
Students: 35961, Price: Paid
Welcome to the ultimate online course on Python for Computer Vision!
This course is your best resource for learning how to use the Python programming language for Computer Vision.
We'll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data.
The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever its necessary for developers to gain the necessary skills to work with image and video data using computer vision.
Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including selfdriving cars, social network apps, medical diagnostics, and many more.
As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.
In this course we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.
We'll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we'll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.
Then we'll move on to understanding video basics with OpenCV, including working with streaming video from a webcam. Afterwards we'll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.
Then we'll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We'll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network.
This course covers all this and more, including the following topics:

NumPy

Images with NumPy

Image and Video Basics with NumPy

Color Mappings

Blending and Pasting Images

Image Thresholding

Blurring and Smoothing

Morphological Operators

Gradients

Histograms

Streaming video with OpenCV

Object Detection

Template Matching

Corner, Edge, and Grid Detection

Contour Detection

Feature Matching

WaterShed Algorithm

Face Detection

Object Tracking

Optical Flow

Deep Learning with Keras

Keras and Convolutional Networks

Customized Deep Learning Networks

State of the Art YOLO Networks

and much more!
Feel free to message me on Udemy if you have any questions about the course!
Thanks for checking out the course page, and I hope to see you inside!
Jose
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 31072, Price: $129.99
Students: 31072, Price: Paid
Welcome to Tensorflow 2.0!
What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.
Tensorflow is Google's library for deep learning and artificial intelligence.
Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photorealistic images of people and things that never existed (GANs)

Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Selfdriving cars (Computer Vision)

Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

Even creating videos of people doing and saying things they never did (DeepFakes  a potentially nefarious application of deep learning)
Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cashrich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
In other words, if you want to do deep learning, you gotta know Tensorflow.
This course is for beginnerlevel students all the way up to expertlevel students. How can this be?
If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).
Current projects include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot
Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are allnew and neverbeforeseen projects in this course such as time series forecasting and how to do stock predictions.
This course is designed for students who want to learn fast, but there are also "indepth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

Deploying a model with Tensorflow Lite (mobile and embedded applications)

Distributed Tensorflow training with Distribution Strategies

Writing your own custom Tensorflow model

Converting Tensorflow 1.x code to Tensorflow 2.0

Constants, Variables, and Tensors

Eager execution

Gradient tape
Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theorydense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
Thanks for reading, and I’ll see you in class!
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 29825, Price: $99.99
Students: 29825, Price: Paid
This course will guide you through how to use Google's latest TensorFlow 2 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 2 framework in a way that is easy to understand.
We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!
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

NumPy Crash Course

Pandas Data Analysis Crash Course

Data Visualization Crash Course

Neural Network Basics

TensorFlow Basics

Keras Syntax Basics

Artificial Neural Networks

Densely Connected Networks

Convolutional Neural Networks

Recurrent Neural Networks

AutoEncoders

GANs  Generative Adversarial Networks

Deploying TensorFlow into Production

and much more!
Keras, a userfriendly API standard for machine learning, will be the central highlevel API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several modelbuilding APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.
TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance
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 deep learning guru today! We'll see you inside the course!
Modern Deep Learning in Python
Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 28277, Price: $109.99
Students: 28277, Price: Paid
This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plugandplay script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.
You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.
You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.
Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.
In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on  what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning  Theano. With this library we will also examine the basic building blocks  variables, expressions, and functions  so that you can build neural networks in Theano with confidence.
Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.
Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPUinstance on AWS and compare the speed of CPU vs GPU for training a deep neural network.
With all this extra speed, we are going to look at a real dataset  the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"
These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

Know about gradient descent

Probability and statistics

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Know how to write a neural network with Numpy
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning: Convolutional Neural Networks in Python
Use CNNs for Image Recognition, Natural Language Processing (NLP) +More! For Data Science, Machine Learning, and AI
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 27466, Price: $109.99
Students: 27466, Price: Paid
*** NOW IN TENSORFLOW 2 and PYTHON 3 ***
Learn about one of the most powerful Deep Learning architectures yet!
The Convolutional Neural Network (CNN) has been used to obtain stateoftheart results in computer vision tasks such as object detection, image segmentation, and generating photorealistic images of people and things that don't exist in the real world!
This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
This course will teach you:

The basics of machine learning and neurons (just a review to get you warmed up!)

Neural networks for classification and regression (just a review to get you warmed up!)

How to model image data in code

How to model text data for NLP (including preprocessing steps for text)

How to build an CNN using Tensorflow 2

How to use batch normalization and dropout regularization in Tensorflow 2

How to do image classification in Tensorflow 2

How to do data preprocessing for your own custom image dataset

How to use Embeddings in Tensorflow 2 for NLP

How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, partsofspeech tagging, named entity recognition)
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
Suggested Prerequisites:

matrix addition and multiplication

basic probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning Prerequisites: Linear Regression in Python
Data science, machine learning, and artificial intelligence in Python for students and professionals
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 27288, Price: $99.99
Students: 27288, Price: Paid
This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to realworld problems. We show you how one might code their own linear regression module in Python.
Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:

deep learning

machine learning

data science

statistics
In the first section, I will show you how to use 1D linear regression to prove that Moore's Law is true.
What's that you say? Moore's Law is not linear?
You are correct! I will show you how linear regression can still be applied.
In the next section, we will extend 1D linear regression to anydimensional linear regression  in other words, how to create a machine learning model that can learn from multiple inputs.
We will apply multidimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.
Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, traintest splits, and so on.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Artificial Neural Network for Regression
Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 26897, Price: Free
Students: 26897, Price: Free
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.
The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.
Go handson with Hadelin in solving this complex, realworld Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browserbased notebook environment that runs completely in the cloud. It’s a gamechanging interface that will supercharge your Machine Learning toolkit.
Check out what’s in store for you when you enroll:
Part 1: Data Preprocessing

Importing the dataset

Splitting the dataset into the training set and test set
Part 2: Building an ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the output layer

Compiling the ANN
Part 3: Training the ANN

Training the ANN model on the training set

Predicting the results of the test set
More about CombinedCycle Power Plants
A combinedcycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot airfuel mixture moves through the blades, making them spin. The fastspinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
Deep Learning and NLP AZ™: How to create a ChatBot
Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python
Created by Hadelin de Ponteves  AI Entrepreneur
Students: 26039, Price: $109.99
Students: 26039, Price: Paid
We've talked about, speculated and often seen different applications for Artificial Intelligence  But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd?
ChatBots are here, and they came change and shapeshift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement.
If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!
Deep Learning Prerequisites: The Numpy Stack in Python V2
Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence
Created by Lazy Programmer Team  Artificial Intelligence and Machine Learning Engineer
Students: 25862, Price: Free
Students: 25862, Price: Free
Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).
The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code.
As I've always said: "If you can't implement it, then you don't understand it".
Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.
This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.
The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.
Suggested Prerequisites:

linear algebra

probability

Python programming
Deep Learning: Recurrent Neural Networks in Python
GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 25311, Price: $99.99
Students: 25311, Price: Paid
*** NOW IN TENSORFLOW 2 and PYTHON 3 ***
Learn about one of the most powerful Deep Learning architectures yet!
The Recurrent Neural Network (RNN) has been used to obtain stateoftheart results in sequence modeling.
This includes time series analysis, forecasting and natural language processing (NLP).
Learn about why RNNs beat oldschool machine learning algorithms like Hidden Markov Models.
This course will teach you:

The basics of machine learning and neurons (just a review to get you warmed up!)

Neural networks for classification and regression (just a review to get you warmed up!)

How to model sequence data

How to model time series data

How to model text data for NLP (including preprocessing steps for text)

How to build an RNN using Tensorflow 2

How to use a GRU and LSTM in Tensorflow 2

How to do time series forecasting with Tensorflow 2

How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!)

How to use Embeddings in Tensorflow 2 for NLP

How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, partsofspeech tagging, named entity recognition)
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

matrix addition, multiplication

basic probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning Prerequisites: Logistic Regression in Python
Data science, machine learning, and artificial intelligence in Python for students and professionals
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 24949, Price: $109.99
Students: 24949, Price: Paid
This course is a leadin to deep learning and neural networks  it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to realworld problems. We show you how one might code their own logistic regression module in Python.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make datadriven decisions and optimize your business using scientific principles, then this course is for you.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 24841, Price: $109.99
Students: 24841, Price: Paid
Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.
This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.
When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.
I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.
Let me give you a quick rundown of what this course is all about:
We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)
We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.
You can imagine that such a task is a basic prerequisite for selfdriving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in realtime)
We’ll be looking at a stateoftheart algorithm called SSD which is both faster and more accurate than its predecessors.
Another very popular computer vision task that makes use of CNNs is called neural style transfer.
This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.
I will also introduce you to the nowfamous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate stateoftheart, photorealistic images.
Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.
I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!
AWESOME FACTS:

One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.

Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on highlevel building blocks. The result? Almost zero math.

Another result? No complicated lowlevel code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

Know how to build, train, and use a CNN using some library (preferably in Python)

Understand basic theoretical concepts behind convolution and neural networks

Decent Python coding skills, preferably in data science and the Numpy Stack
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Deep Learning: Advanced NLP and RNNs
Natural Language Processing with Sequencetosequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 20890, Price: $124.99
Students: 20890, Price: Paid
It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you'll be able to build applications for problems like:

text classification (examples are sentiment analysis and spam detection)

neural machine translation

question answering
We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:

bidirectional RNNs

seq2seq (sequencetosequence)

attention

memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the highlevel concepts. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class!
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

Decent Python coding skills

Understand RNNs, CNNs, and word embeddings

Know how to build, train, and evaluate a neural network in Keras
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Blockchain and Deep Learning: Future of AI
Jobs Training for the Future
Created by Melanie Swan  Blockchain Theorist, New School University, NYC
Students: 19814, Price: Free
Students: 19814, Price: Free
This course provides a conceptual overview and technical summary of the two top job growth areas worldwide: blockchain technology and deep learning. The course discusses how these technologies may be used together in deep learning chains. Some of the important application areas are autonomous driving, health care, energy, and finance.
Basics of Deep Learning
Fundamentals of Neural Network
Created by Sunil Kumar Mishra  AI Enthusiast  Startup Mentor I Author
Students: 19053, Price: Free
Students: 19053, Price: Free
Have you ever wondered what is Deep Learning and how it is helping today in powering Artificial Intelligence?
This basic course in Deep Learning may unravel some of them. You dont need any technical or coding background to know the basic fundamentals of Neural Network. This course is designed for functional consultants, product managers as well as developers and architects.
Contents of the course:
1. Inspiration for Deep Learning
2. Key Concepts of Deep Learning
3. Improving the model
4. Convolutional network
5. Recurrent network
6. Word representation
The Complete SelfDriving Car Course – Applied Deep Learning
Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python
Created by Rayan Slim  Teacher
Students: 18397, Price: $119.99
Students: 18397, Price: Paid
Selfdriving cars have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector.
Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a selfdriving car, one of the most disruptive technologies in the world today.
Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.
You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.
By the end of the course, you will have built a fully functional selfdriving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company.
This course will show you how to:

Use Computer Vision techniques via OpenCV to identify lane lines for a selfdriving car.

Learn to train a Perceptronbased Neural Network to classify between binary classes.

Learn to train Convolutional Neural Networks to identify between various traffic signs.

Train Deep Neural Networks to fit complex datasets.

Master Keras, a power Neural Network library written in Python.

Build and train a fully functional self driving car to drive on its own!
No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.
This course also comes with all the source code and friendly support in the Q&A area.
PyTorch for Deep Learning with Python Bootcamp
Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library!
Created by Jose Portilla  Head of Data Science, Pierian Data Inc.
Students: 15809, Price: $109.99
Students: 15809, Price: Paid
Welcome to the best online course for learning about Deep Learning with Python and PyTorch!
PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
This course focuses on balancing important theory concepts with practical handson exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations.
In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including:

NumPy

Pandas

Machine Learning Theory

Test/Train/Validation Data Splits

Model Evaluation  Regression and Classification Tasks

Unsupervised Learning Tasks

Tensors with PyTorch

Neural Network Theory

Perceptrons

Networks

Activation Functions

Cost/Loss Functions

Backpropagation

Gradients


Artificial Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

and much more!
By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets.
So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I'll see you inside the course!
Jose
Recommender Systems and Deep Learning in Python
The most indepth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
Created by Lazy Programmer Inc.  Artificial intelligence and machine learning engineer
Students: 15221, Price: $109.99
Students: 15221, Price: Paid
Believe it or not, almost all online businesses today make use of recommender systems in some way or another.
What do I mean by “recommender systems”, and why are they useful?
Let’s look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook.
Recommender systems form the very foundation of these technologies.
Google: Search results
They are why Google is the most successful technology company today.
YouTube: Video dashboard
I’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?
That’s right. Recommender systems!
Facebook: So powerful that world governments are worried that the newsfeed has too much influence on people! (Or maybe they are worried about losing their own power... hmm...)
Amazing!
This course is a big bag of tricks that make recommender systems work across multiple platforms.
We’ll look at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank.
We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.
But this course isn’t just about news feeds.
Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.
These algorithms have led to billions of dollars in added revenue.
So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.
For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised and unsupervised learning  Autoencoders and Restricted Boltzmann Machines), and you’ll learn a bag full of tricks to improve upon baseline results.
As a bonus, we will also look how to perform matrix factorization using big data in Spark. We will create a cluster using Amazon EC2 instances with Amazon Web Services (AWS). Most other courses and tutorials look at the MovieLens 100k dataset  that is puny! Our examples make use of MovieLens 20 million.
Whether you sell products in your ecommerce store, or you simply write a blog  you can use these techniques to show the right recommendations to your users at the right time.
If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!
I’ll see you in class!
NOTE:
This course is not "officially" part of my deep learning series. It contains a strong deep learning component, but there are many concepts in the course that are totally unrelated to deep learning.
"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:

For earlier sections, just know some basic arithmetic

For advanced sections, know calculus, linear algebra, and probability for a deeper understanding

Be proficient in Python and the Numpy stack (see my free course)

For the deep learning section, know the basics of using Keras

For the RBM section, know Tensorflow
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)