Best Neural Networks Courses

Find the best online Neural Networks Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Neural Networks Courses.

Deep Learning A-Z™: Hands-On 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

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Students: 309032, Price: $89.99

Students: 309032, Price:  Paid

*** As seen on Kickstarter ***

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving 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 A-Z? ---

Here are five reasons we think Deep Learning A-Z™ 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 hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.

3. EXCITING PROJECTS

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you're in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing 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. HANDS-ON CODING 

In Deep Learning A-Z™ 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 step-by-step 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. IN-COURSE 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 open-source 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 ---

Scikit-learn the most practical Machine Learning library. We will mainly use it:  

  • to evaluate the performance of our models with the most relevant technique, k-Fold 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 hands-on 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 A-Z™ 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 A-Z™ 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 A-Z™ you will master some of the most cutting-edge 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 hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.

--- Real-World 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 real-world 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 geo-demographical 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 long-term 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 cutting-edge deep learning methods in our course! 

In this part you will learn how to implement this ultra-powerful 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 top-paid 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 real-World case studies. 

We are super enthusiastic about Deep Learning and hope to see you inside the class!

Kirill & Hadelin

Introduction to networking for complete beginners

Learn elementary networking concepts in less than 60 minutes. Great before going for General Networking, MTA or CCNA

Created by Marious Kuriata - IT Exam Practice, Network Engineer and Instructor

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Students: 189444, Price: Free

Students: 189444, Price:  Free

You know nothing about networking? Not ready for MTA Servers / Networking, Comptia, VPN, General Networking courses?

The solution is simple: "Introduction to networking for complete beginners".

More than 80.000 students have joined this training! 5000+ reviews. Start today!

In this course you will learn elementary networking concepts in 60 minutes.

  • Why do we need networks?
  • Basic commands: ipconfig, ping, traceroute
  • Introduction to the world of Cisco
  • How to start troubleshooting a home network
  • Wireless issues
  • How to start a career in IT
  • Bonus videos

The course is a perfect starting point before going for other courses I prepared for you like General Networking, MTA in Networking or CCNA.

It is free for a limited time only!

Complete beginners guide to networking concepts. Join today

Contents and Overview

In 60 minutes of content including more than 15 lectures, this course covers the basics of networking concepts for complete beginners. Enjoy free bonus videos covering additional topics!

Students completing the course will have the knowledge to join courses like General Networking, MTA Servers Fundamentals, MTA Networking Fundamentals, VPNs or CCNA. A lot of real world examples help you apply your skills and make difficult topics much easier to understand.

Join today!

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on 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

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Students: 150695, Price: $89.99

Students: 150695, Price:  Paid

New! Updated for 2021 with extra content on generative models: variational auto-encoders (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 top-paying 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 hands-on 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 Auto-Encoders (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

  • K-Means Clustering

  • Principal Component Analysis

  • Train/Test and cross validation

  • Bayesian Methods

  • Decision Trees and Random Forests

  • Multiple Regression

  • Multi-Level Models

  • Support Vector Machines

  • Reinforcement Learning

  • Collaborative Filtering

  • K-Nearest 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 Windows-based 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 real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Artificial Neural Networks (ANN) with Keras in Python and R

Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

"]

Students: 119295, Price: $19.99

Students: 119295, Price:  Paid

You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?

You've found the right Neural Networks course!

After completing this course you will be able to:

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

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

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

  • Confidently practice, discuss and understand Deep Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create a predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 - Python and R basics

    This part gets you started with Python.

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

  • Part 2 - Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 - Creating Regression and Classification ANN model in Python and R

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 - Data Preprocessing

    In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

    In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like  missing value imputation, variable transformation and Test-Train split.

By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

------------

Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, 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.

So You Want to be a Network Engineer?

Find out which IT certification is right for you with these sample lessons. From beginner to advanced.

Created by Paul Browning - Cisco Engineer and Internet Entrepreneur

"]

Students: 99659, Price: Free

Students: 99659, Price:  Free

If you are looking to make a career change into IT or get ahead in your current IT role you need to get certified. But which career path is for you? Network support, desktop support, core router and switch support, voice networking, security engineer...the choice is overwhelming.

This series of taster lessons will let you dip your toes into a large number of courses to see which one you like the best. The courses take you from beginner level (A+ to up advanced with Cisco CCNP).

Convolutional Neural Networks in Python: CNN Computer Vision

Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

"]

Students: 97264, Price: $19.99

Students: 97264, Price:  Paid

You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?

You've found the right Convolutional Neural Networks course!

After completing this course you will be able to:

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

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

  • Confidently practice, discuss and understand Deep Learning concepts

  • Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.

If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 (Section 2)- Python basics

    This part gets you started with Python.

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

  • Part 2 (Section 3-6) - ANN Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 (Section 7-11) - Creating ANN model in Python

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 (Section 12) - CNN Theoretical Concepts

    In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.

    In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.

  • Part 5 (Section 13-14) - Creating CNN model in Python
    In this part you will learn how to create CNN models in Python.

    We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.

  • Part 6 (Section 15-18) - End-to-End Image Recognition project in Python
    In this section we build a complete image recognition project on colored images.

    We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).

By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

------------

Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, 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.

Neural Networks in Python: Deep Learning for Beginners

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

"]

Students: 88703, Price: $19.99

Students: 88703, Price:  Paid

You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?

You've found the right Neural Networks course!

After completing this course you will be able to:

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

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

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

  • Confidently practice, discuss and understand Deep Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create a predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 - Python basics

    This part gets you started with Python.

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

  • Part 2 - Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 - Creating Regression and Classification ANN model in Python

    In this part you will learn how to create ANN models in Python.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 - Data Preprocessing

    In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

    In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like  missing value imputation, variable transformation and Test-Train split.

  • Part 5 - Classic ML technique - Linear Regression
    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don't understand

    it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

------------

Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

    In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

    In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

    In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, 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.

Artificial Neural Networks for Business Managers in R Studio

You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

"]

Students: 86577, Price: $19.99

Students: 86577, Price:  Paid

You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?

You've found the right Neural Networks course!

After completing this course you will be able to:

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

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

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

  • Confidently practice, discuss and understand Deep Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create a predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

  • Part 1 - Setting up R studio and R Crash course

    This part gets you started with R.

    This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.

  • Part 2 - Theoretical Concepts

    This part will give you a solid understanding of concepts involved in Neural Networks.

    In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Part 3 - Creating Regression and Classification ANN model in R

    In this part you will learn how to create ANN models in R Studio.

    We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

    We also understand the importance of libraries such as Keras and TensorFlow in this part.

  • Part 4 - Data Preprocessing

    In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

    In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like  missing value imputation, variable transformation and Test-Train split.

  • Part 5 - Classic ML technique - Linear Regression
    This section starts with simple linear regression and then covers multiple linear regression.

    We have covered the basic theory behind each concept without getting too mathematical about it so that you

    understand where the concept is coming from and how it is important. But even if you don't understand

    it,  it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

    We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.

By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

Start-Tech Academy

------------

Below are some popular FAQs of students who want to start their Deep learning journey-

Why use R for Deep Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing 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, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Data Science: Deep Learning and Neural Networks in Python

The MOST in-depth 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 full-on non-linear 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 GPU-optimization, check out my follow-up 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 NetworksRestricted Boltzmann MachinesAutoencoders, 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)

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, photo-realistic 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)

  • Self-driving 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 cash-rich 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 beginner-level students all the way up to expert-level 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 all-new and never-before-seen 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 "in-depth" 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 theory-dense 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)

Learn Graphs and Social Network Analytics Using Python

This course is absolutely designed for beginners , graph enthusiast ready to analyze the world using graphs

Created by Theophilus Siameh - Spark Developer ,Kafka and Data Scientist

"]

Students: 30835, Price: Free

Students: 30835, Price:  Free

BRAND NEW COURSE IS HERE ! Learn Graphs and Social Network Analytics .Become a graph and social analyst today. This is a comprehensive course , simple and straight forward for python enthusiast and those with little python background. You want to learn about how to draw graphs and analyze them, this is the course for you. This course will contain some quizzes, test and some homework assignments, as well as some real world assignment projects. There is over 55 lectures and about 6hours to complete the course. This course comes with live coding screenshots using iPython Notebook .Below is the list of the course summary

- Overivew of networkX

- Install networkX module and iPython Notebooks

- Create nodes

- Add edges to nodes

- Getting attributes from a graph

- Manipulate your graphs ie.; remove nodes /edges

- Create DiGraphs/MultiGraphs/MultiDiGraphs

- Graph Generators

- Graph metrics ; shortest path/clustering coefficient

- Define functions

- Visualize graphs

- Calculate nodes/degree/centrality metrics

- Some random graphs

- Small famous graphs

- Reading and writing graph files

- Social network analysis

- Subgraphs

- Facebook Social Network Analysis

Course goals :

-At the end of the course students should be able to learn some basics of graph theory

- Students should be able to analyze Facebook social networks

- Students should take the simple quizzes

- Students should know what is directed and undirected graphs

- Students should be able to visualize graphs using different graph plots

- You can use this course to analyze the world as a network

- Everything in this world is now connected

- Extract useful information from graphs

Life time access to the course. What are you waiting for? Learn practical graph and social network analytics today that would improve your career and increase your knowledge.

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 state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic 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, parts-of-speech 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)

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 hands-on with Hadelin in solving this complex, real-world 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, browser-based notebook environment that runs completely in the cloud. It’s a game-changing 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 Combined-Cycle Power Plants

A combined-cycle 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 air-fuel mixture moves through the blades, making them spin. The fast-spinning 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.

Complete 2020 Data Science & Machine Learning Bootcamp

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!

Created by Philipp Muellauer - Data Scientist | Android Developer | Teacher

"]

Students: 26104, Price: $89.99

Students: 26104, Price:  Paid

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:

  • The course is a taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp.

  • In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.

  • This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.

  • The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.

  • To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.

  • You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

In the curriculum, we cover a large number of important data science and machine learning topics, such as:

  • Data Cleaning and Pre-Processing

  • Data Exploration and Visualisation

  • Linear Regression

  • Multivariable Regression

  • Optimisation Algorithms and Gradient Descent

  • Naive Bayes Classification

  • Descriptive Statistics and Probability Theory

  • Neural Networks and Deep Learning

  • Model Evaluation and Analysis

  • Serving a Tensorflow Model

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

  • Python 3

  • Tensorflow

  • Pandas

  • Numpy

  • Scikit Learn

  • Keras

  • Matplotlib

  • Seaborn

  • SciPy

  • SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:

  • Data Types and Variables

  • String Manipulation

  • Functions

  • Objects

  • Lists, Tuples and Dictionaries

  • Loops and Iterators

  • Conditionals and Control Flow

  • Generator Functions

  • Context Managers and Name Scoping

  • Error Handling

By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.

Sign up today, and look forward to:

  • 178+ HD Video Lectures

  • 30+ Code Challenges and Exercises

  • Fully Fledged Data Science and Machine Learning Projects

  • Programming Resources and Cheatsheets

  • Our best selling 12 Rules to Learn to Code eBook

  • $12,000+ data science & machine learning bootcamp course materials and curriculum

Don't just take my word for it, check out what existing students have to say about my courses:

“One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I'm only half way through but I feel like it is some of the best money I've ever spent.” -Robert Vance

“I've spent £27,000 on University..... Save some money and buy any course available by Philipp! Great stuff guys.” -Terry Woodward

"This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it's not boring to follow throughout the whole course. Keep up the good work guys!" - Marvin Septianus

“Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow” -Lenox James

“Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.” -Andres Ariza

“I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program” -Isaac Barnor

“I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.” -Dale Barnes

“This course has been amazing. Thanks for all the info. I'll definitely try to put this in use. :)” -Devanshika Ghosh

“Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial” -Bimal Becks

“English is not my native language but in this video, Phillip has great pronunciation so I don't have problem even without subtitles :)” -Dreamerx85

“Clear, precise and easy to follow instructions & explanations!” -Andreea Andrei

“An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.” -Ian

REMEMBER… I'm so confident that you'll love this course that we're offering a FULL money back guarantee for 30 days! So it's a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.

So what are you waiting for? Click the buy now button and join the world's best data science and machine learning course.

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

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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 state-of-the-art results in sequence modeling.

This includes time series analysis, forecasting and natural language processing (NLP).

Learn about why RNNs beat old-school 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, parts-of-speech 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)

Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs

Sequence prediction course that covers topics such as: RNN, LSTM, GRU, NLP, Seq2Seq, Attention, Time series prediction

Created by Jad Slim - Developer

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Students: 20158, Price: Free

Students: 20158, Price:  Free

This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.

We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.

You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.

Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.

Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!

Unsupervised Deep Learning in Python

Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

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Students: 17529, Price: $34.99

Students: 17529, Price:  Paid

This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!

In these course we’ll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.

Last, we’ll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I’ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD-k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity.

Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found.

All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and Python coding. You'll want to install Numpy, Theano, and Tensorflow for this course. These are essential items in your data analytics toolbox.

If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, 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

  • linear algebra

  • probability

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

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

  • can write a feedforward neural network in Theano or 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)

Tensorflow.JS crash course 2020

This course is for all those people who wants to get a brief idea on Tensorflow.JS in 2020

Created by Muhammad Jamal Butt - Computer Scientist

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Students: 10926, Price: Free

Students: 10926, Price:  Free

This course will give you a brief idea in understanding the flow of Tensorflow JS.  I will go through all the steps needed in creating a basic neural network on the browser. Tensorflow JS will provide us with the basic pre-built function, that will help us in creating and using browser to train 'Machine Learning' based models.

Self-driving go-kart with Unity-ML

Deep learning applied to a self-driving car simulation

Created by Fabrizio Frigeni - Engineer

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Students: 10780, Price: Free

Students: 10780, Price:  Free

WARNING: take this class as a gentle introduction to machine learning, with particular focus on machine vision and reinforcement learning. The Unity project provided in this course is now obsolete because the Unity ML agents library is still in its beta version and the interface keeps changing all the time! Some of the implementation details you will find in this course will look different if you are using the latest release, but the key concepts and the background theory are still valid. Please refer to the official migrating documentation on the ml-agents github for the latest updates.

Learn how to combine the beauty of Unity with the power of Tensorflow to solve physical problems in a simulated environment with state-of-the-art machine learning techniques.

We study the problem of a go-kart racing around a simple track and try three different approaches to control it: a simple PID controller; a neural network trained via imitation (supervised) learning; and a neural network trained via deep reinforcement learning.

Each technique has its strengths and weaknesses, which we first show in a theoretical way at simple conceptual level, and then apply in a practical way. In all three cases the go-kart will be able to complete a lap without crashing.

We provide the Unity template and the files for all three solutions. Then see if you can build on it and improve performance further more.

Buckle up and have fun! 

Tensorflow and Keras For Neural Networks and Deep Learning

Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

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Students: 10279, Price: $89.99

Students: 10279, Price:  Paid

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!

It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning  using two of the most important Deep Learning frameworks- Tensorflow and Keras.                         

HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:

This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python..

This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning...

By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources  using data science related techniques and producing publications for international peer reviewed journals.

 Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning..

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework.

Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks!

DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages
• Brief introduction to the working of Pandas and Numpy
• The basics of the Tensorflow syntax and graphing environment
• The basics of the Keras syntax
• Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks
• You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras

BUT,  WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE:

You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !!

The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing  data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different  techniques on real data and interpret the results..

After each video you will learn a new concept or technique which you may apply to your own projects!

JOIN THE COURSE NOW!

Learn Keras: Build 4 Deep Learning Applications

Get up and running with deep learning with keras, a high level deep learning API

Created by Adam Eubanks - Self Taught Programmer And Learning Enthusiast

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Students: 10135, Price: Free

Students: 10135, Price:  Free

When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions. In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.

Learn Keras: Build 4 Deep Learning Applications is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:

1. Linear Regression

2. Dense Neural Networks

3. Convolutional Neural Networks

4. Recurrent Neural Networks

In conclusion, if you are looking at a quick intro into deep learning, this course is for you.

So what are you waiting for? Let's get started!

Image Recognition with Neural Networks From Scratch

Write An Image Recognition Program in Python

Created by Long Nguyen - Faculty

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Students: 9593, Price: $59.99

Students: 9593, Price:  Paid

This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus. Although the prerequisite is minimal, we will discuss many advanced topics including:

1) functions and their computational graphs.

2) neural networks

3) conceptually understand the derivative and the gradient.

4) gradient descent and backpropagation

5) the multivariable chain rule

6) mini-batch gradient descent

[2021] Machine Learning and Deep Learning Bootcamp in Python

Machine Learning models, Neural Networks, Deep Learning and Reinforcement Learning Approaches in Keras and TensorFlow

Created by Holczer Balazs - Software Engineer

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Students: 7631, Price: $119.99

Students: 7631, Price:  Paid

This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. 

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.

  • Machine Learning Algorithms: machine learning approaches are becoming more and more important even in 2020. In this course, you can learn about:

    1. linear regression model

    2. logistic regression model

    3. k nearest neighbour classifier

    4. naive Bayes classifier

    5. support vector machines (SVMs)

    6. random forest classifier

    7. boosting algorithm

    8. principle components analysis (PCA)

  • Machine Learning approaches in finance: how to use learning algorithms to predict stock prices

  • Computer Vision and Face Detection with OpenCV

  • Neural Networks: what are feed-forward neural networks and why are they useful

  • Deep Learning: feedforward neural networks and deep neural networks are the state-of-the-art approaches in artificial intelligence in 2020. So what are the topics you will learn in this course?

    1. deep neural networks

    2. convolutional neural networks (CNNs)

    3. recurrent neural networks (RNNs)

  • Recurrent Neural Networks and Convolutional Neural Networks and their applications such as sentiment analysis or stock prices forecast

  • Reinforcement Learning: Markov Decision processes (MDPs) and Q-learning

  • Tic Tac Toe game with Q learning approach and the deep Q learning approach

Thanks for joining the course, let's get started!

How To Network

- Becoming a more confident and effective networker

Created by Alex Swallow - The Influence Expert- Helping You Grow Your Influence

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Students: 7334, Price: Free

Students: 7334, Price:  Free

This course is for anyone who wants to feel more confident about networking and who feels that they could be a more effective networker.

Networking is something that many people find scary or difficult, but it doesn't need to be either.

It's a crucial skill to master if you want to progress in your career and to make the difference you want to in the world.

In the course, we will cover both offline and online networking and look at my '3 P's' Method which will help you be at your best when you attend a networking event. We will look at how to make progress over time, how to help others through networking, how to be memorable- and we will start putting together an actionable plan to make sure that your development continues beyond the course.

I look forward to helping you discover 'How To Network'!

The Complete Neural Networks Bootcamp: Theory, Applications

Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!

Created by Fawaz Sammani - Computer Vision Researcher

"]

Students: 6758, Price: $94.99

Students: 6758, Price:  Paid

This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework!

The course includes the following Sections:

--------------------------------------------------------------------------------------------------------

Section 1 - How Neural Networks and Backpropagation Works

In this section, you will deeply understand the theories of how neural networks  and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages!

Section 2 - Loss Functions

In this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work.

Section 3 - Optimization

In this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMSProp, Adam, AMSGrad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others.

Section 4 - Weight Initialization

In this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization.

Section 5 - Regularization Techniques

In this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout. We'll also talk about normalization as well as batch normalization and Layer Normalization.

Section 6- Introduction to PyTorch

In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!

Section 7 - Practical Neural Networks in PyTorch - Application 1

In this section, you will apply what you've learned to build a Feed Forward Neural Network to classify handwritten digits. This is the first application of Feed Forward Networks we will be showing.

Section 8 - Practical Neural Networks in PyTorch - Application 2

In this section, we will build a feed forward Neural Network to classify weather a person has diabetes or not. We will train the network on a large dataset of diabetes!

Section 9 - Visualize the Learning Process

In this section, we will visualize how neural networks are learning, and how good they are at separating non-linear data!

Section 10 - Implementing a Neural Network from Scratch with Python and Numpy

In this section, we will understand and code up a neural network without using any deep learning library (from scratch using only python and numpy). This is necessary to understand how the underlying structure works.

Section 11 - Convolutional Neural Networks

In this section, we will introduce you to Convolutional Networks that are used for images. We will show you first the relationship to Feed Forward Networks, and then we will introduce you the concepts of Convolutional Networks one by one!

Section 12 - Practical Convolutional Networks in PyTorch

In this section, we will apply Convolutional Networks to classify handwritten digits. This is the first application of CNNs we will do.

Section 13- Deeper into CNN: Improving and Plotting

In this section, we will improve the CNN that we built in the previous section, as well show you how to plot the results of training and testing! Moreover, we will show you how to classify your own handwritten images through the network!

Section 14 - CNN Architectures

In this section, we will introduce the CNN architectures that are widely used in all deep learning applications. These architectures are: AlexNet, VGG net, Inception Net, Residual Networks and Densely Connected Networks. We will also discuss some object detection architectures.

Section 15- Residual Networks

In this section, we will dive deep into the details and theory of Residual Networks, and then we'll build a Residual Network in PyTorch from scratch!

Section 16 - Transfer Learning in PyTorch - Image Classification

In this section, we will apply transfer learning on a Residual Network, to classify ants and bees. We will also show you how to use your own dataset and apply image augmentation. After completing this section, you will be able to classify any images you want!

Section 17- Convolutional Networks Visualization

In this section, we will visualize what the neural networks output, and what they are really learning. We will observe the feature maps of the network of every layer!

Section 18 - YOLO Object Detection (Theory)

In this section, we will learn one of the most famous Object Detection Frameworks: YOLO!! This section covers the theory of YOLO in depth.

Section 19 - Autoencoders and Variational Autoencoders

In this section, we will cover Autoencoders and Denoising Autoencoders. We will then see the problem they face and learn how to mitigate it with Variational Autoencoders.

Section 20 - Recurrent Neural Networks

In this section, we will introduce you to Recurrent Neural Networks and all their concepts. We will then discuss the Backpropagation through  time, the vanishing gradient problem, and finally about Long Short Term Memory (LSTM) that solved the problems RNN suffered from.

Section 21 - Word Embeddings

In this section, we will discuss how words are represented as features. We will then show you some Word Embedding models.  We will also show you how to implement word embedding in PyTorch!

Section 22 - Practical Recurrent Networks in PyTorch

In this section, we will apply Recurrent Neural Networks using LSTMs in PyTorch to generate text similar to the story of Alice in Wonderland! You can just replace the story with any other text you want, and the RNN will be able to generate text similar to it!

Section 23 - Sequence Modelling

In this section, we will learn about Sequence-to-Sequence Modelling. We will see how Seq2Seq models work and where they are applied. We'll also talk about Attention mechanisms and see how they work.

Section 24 - Practical Sequence Modelling in PyTorch - Build a Chatbot

In this section, we will apply what we learned about sequence modeling and build a Chatbot with Attention Mechanism.

Section 25 - Saving and Loading Models

In this section, we will show you how to save and load models in PyTorch, so you can use these models either for later testing, or for resuming training!

Section 26 - Transformers

In this section, we will cover the Transformer, which is the current state-of-art model for NLP and language modeling tasks. We will go through each component of a transformer.

Section 27 - Build a Chatbot with Transformers

In this section, we will implement all what we learned in the previous section to build a Chatbot using Transformers.

Computer Vision For iOS Developers Course

Learn how to train Object Detection and Semantic Segmentation models for mobile, and integrate them into your app.

Created by Alexey Korotkov - CEO at MakeML

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Students: 5950, Price: Free

Students: 5950, Price:  Free

Welcome to Computer Vision for iOS developers Course.
In this course, you'll learn the basics needed to understand Object Detection and Semantic Segmentation, and by the end of the course, you'll be able to train models that you can use in your apps.

We will cover the next topics in this course:

1) What is Computer Vision
2) What is Object Detection and Semantic Segmentation
3) Tools for Creating Image Datasets and labeling them
4) Image Dataset Augmentation
5) Tools and Environments for training neural networks
6) Integration of CoreML and TFLite models into iOS apps
7) 2 projects that use Computer Vision in real-world applications

This course is made using https://makeml.app product.

Simplified: Intro to Machine Learning

Learn what machine learning is all about in this beginner-level entry course

Created by Jayanth Peetla - High School Student at the Academies of Loudoun

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Students: 4413, Price: Free

Students: 4413, Price:  Free

This is the first of a series of courses dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning.  Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require.  Why learn machine learning?  Artificial intelligence has already established itself as the future of modern society.  Experts predict that up to 20 million jobs will be lost to AI by 2030.  Therefore, to stay competitive in the constantly changing labor force, it's critical to keep up with new technology.  Machine learning, one of the biggest sectors of artificial intelligence, has shown to be a promising, newly emerging field in the tech industry.  After completing this course and the rest of the courses in the series, you will have an in-depth understanding of machine learning.

Machine Learning™ – Neural Networks from Scratch [Python]

Learn Hopfield networks and neural networks (and back-propagation) theory and implementation in Python

Created by Holczer Balazs - Software Engineer

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Students: 4280, Price: $19.99

Students: 4280, Price:  Paid

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.

Section 1:

  • what are Hopfield neural networks

  • modeling the human brain

  • the big picture behind Hopfield neural networks

Section 2:

  • Hopfield neural networks implementation

  • auto-associative memory with Hopfield neural networks

Section 3:

  • what are feed-forward neural networks

  • modeling the human brain

  • the big picture behind  neural networks

Section 4:

  • feed-forward neural networks implementation

  • gradient descent with back-propagation

In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.

If you are keen on learning machine learning methods, let's get started!

Simplified: All About Neural Networks

Learn all about neural networks in this second installation of the machine learning simplified series.

Created by Jayanth Peetla - High School Student at the Academies of Loudoun

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Students: 3913, Price: Free

Students: 3913, Price:  Free

In this sequel series, we will learn the basics of what a neural network is, how they're used in the real world, and two in-depth projects that allow us to use our skills in an applicable program. This course is dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning.  Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require. In fact, the only major pre-requisite for taking this course is taking the first course in this series which is also available on Udemy for free. This course is divided into four major sections. The first one covers a brief introduction into neural networks and how they're used in the real world. The second section goes in depth about the structure and mechanisms of neural networks. In the third section, we program a fully functional neural network using Spyder from Anaconda Navigator. Lastly, we conclude the course by discussing two types of special neural networks. Machine learning is a critical concept that is becoming very relevant in the status quo. So, before it's too late, join Simplified: All About Neural Networks and learn this topic as simply as possible!

The Visual Guide on How Neural Networks Learn from Data

The BEST Resource for Understanding Neural Networks and How They Learn

Created by Mauricio Maroto - +10,000 students and growing!

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Students: 3157, Price: $89.99

Students: 3157, Price:  Paid

Course Achievements (January 2021): 

  • +3,100 Worldwide Students enrolled

  • Trophy Awards for Key Section Achievements!

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Some Student Reviews are:

"This is an excellent course for people who wish to get an introductory experience in learning about Neural Networks." (December 2020).

"This is a very good introduction on how ANN work. It helps build intuition both on the backpropagation and the math behind it." (January 2020).

"This course does what it claims to do very well." (October 2019)

"Very structured and logical" (July 2018).

"Enlightening overview of how neural networks operate mathematically." (March 2018).

"I just loved this course. The course is very well taught and is divided in easy-to-digest units." (December 2017).

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Hi. Thanks for showing interest in this course!

What makes this course special: 

  • Step-by-Step Neural Network Learning Process,

  • Master topics like Fundamentals, Objectives, Required Datasets, Weights, Biases, Nodes, Activation functions, Feed-Forward Passes, Predictions, Losses, Gradient Descent, Learning, Backpropagation and more!

  • Plus, personalized feedback and help. You ask, I answer directly!

This is your BEST resource for Neural Networks (NN) learning! A must for understanding special concepts and not get lost in computing your own NNs:

✅ First:

You'll start the Neural Networks Primer with Fundamentals, Objectives, Data and more:

  • Learn concepts using analogies for maximum learning, so you will be fully covered. 

  • Learning how NNs learn will be easy with this Primer under your sleeve!

✅ Second: 

You'll continue the NN Primer with Learning, Backpropagation and Predictions and more topics

  • In an easy and intuitive way, you will understand how they work, 

  • This is fundamental in the NN Learning Process. 

  • At the end of this section, you will have mastered the NN Primer!

  • Now, you are ready for the Step-by-Step (in-Motion) sections!


✅ Third:

You'll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes:

  • See how they work inside an NN,

  • Step-by-step templates, so you can follow every detail, 

  • These files will be dynamic, so you'll understand how NNs work as numbers will be updated on-the-fly and right in front of your eyes.

✅ Forth:

You'll continue with the in-Motion section with NN Learning, Backpropagation, Tuning and Prediction:

  • You will understand how NNs learn from the data. 

  • This all part of the dynamic templates you get to keep. 

  • You'll do several examples along the way for maximum learning. 

  • Lastly, you'll see  what NNs do to make the best predictions.


✅ Fifth:

You'll finish the in-Motion section by doing a complete rundown on everyting you've learned so far:

  • You'll see how all NN inner components work for learning and prediction. 

  • Pay close attention at how all parts adjust, making the NN learn in front of your eyes. 

  • After this section, you will be fully versed on how NNs learn!

✅ Sixth:

I will devote a section for more additional knowledge and resources for continous learning. And then, I will conclude with some Final Words.

What are the Requirements?

  • The only thing you'll need for this course is: Excel and PowerPoint: It is that easy! 

  • You will also need to bring your Basic Maths too,

  • If you bring your Calculus (Derivatives) knowledge, that will be a big plus for you (but not required),

What are some of the Benefits?

  • As it is usual in my courses, you will get all files and spreadsheets for all lectures. 

  • This way you can replicate everything I do immediately after each lecture.

  • Neural Networks are the new thing today. 

  • With it, you can explore and engage Artificial Intelligence, which I recommend you to dive in as it's part of the future. 

  • Plus, it's very rewarding and fun  too!

  • New content coming in the near future, let me know yout thoughts.

Lastly, you can post questions or doubts, and I’ll answer to you personally. 

I hope you find this course as useful as I have creating it! 

I’ll see you inside,

-M.A. Mauricio M.