Deep Learning and Data Science courses on sale for $10! April 24, 2017 Today, Udemy has decided to do yet another AMAZING$10 promo.

As usual, I’m providing $10 coupons for all my courses in the links below. Please use these links and share them with your friends! The$10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until April 29. Don’t wait!

At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $10 too! If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order Here are the links for my courses: Deep Learning Prerequisites: Linear Regression in Python https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=APR456 Deep Learning Prerequisites: Logistic Regression in Python https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=APR456 Deep Learning in Python https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=APR456 Practical Deep Learning in Theano and TensorFlow https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=APR456 Deep Learning: Convolutional Neural Networks in Python https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=APR456 Unsupervised Deep Learning in Python https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=APR456 Deep Learning: Recurrent Neural Networks in Python https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=APR456 Advanced Natural Language Processing: Deep Learning in Python https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=APR456 Advanced AI: Deep Reinforcement Learning in Python https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=APR456 Easy Natural Language Processing in Python https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=APR456 Cluster Analysis and Unsupervised Machine Learning in Python https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=APR456 Unsupervised Machine Learning: Hidden Markov Models in Python https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=APR456 Data Science: Supervised Machine Learning in Python https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=APR456 Bayesian Machine Learning in Python: A/B Testing https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=APR456 Ensemble Machine Learning in Python: Random Forest and AdaBoost https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=APR456 Artificial Intelligence: Reinforcement Learning in Python https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=APR456 SQL for Newbs and Marketers https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=APR456 And last but not least,$10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning:

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Python http://bit.ly/2pbXxXz
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Calc 2 http://bit.ly/2oXnhpX
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Remember, these links will self-destruct on April 29 (5 days). Act NOW!

P.S. As you know, I’m ALWAYS updating my courses based on feedback and adding new material. Sometimes, even stuff that has been only recently invented!

Here are a list of recent updates:

– Deep Learning pt 1: Backpropagation troubleshooting (added to appendix). Use this if you have questions like “why do we sum over ‘k prime’?”, and “what is the chain rule?”

– Recurrent Neural Networks (Deep Learning pt 5) and Deep NLP (Deep Learning pt 6): All language-modeling code can now train on the Brown corpus, which you can import directly from NLTK! No need to download and process Wikipedia data dumps anymore! This will make running the code much easier!

– Deep Learning pt 2: Added code samples for grid search and random search, as well as a simple intuitive example of how dropout “emulates” an ensemble (which, by the way, you can gain even FURTHER insight into by taking my Ensemble Machine Learning course!)

And coming very soon (next couple days):

– Deep Learning pt 1: Using SKLearn so using a neural network is just 3 lines of code!

– Recurrent Neural Networks (Deep Learning pt 5) and Deep NLP (Deep Learning pt 6): More discussion about why Tensorflow isn’t appropriate here, but at the same time, adding more Tensorflow examples!

– Linear Regression and Logistic Regression: how to interpret the weights

Udemy $10 coupons April 2017 April 6, 2017 Today, Udemy has decided to do yet another AMAZING$10 promo.

As usual, I’m providing $10 coupons for all my courses in the links below. Please use these links and share them with your friends! The$10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until April 12. Don’t wait!

At the end of this post, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for $10 too! If you don’t know what order to take the courses in, please check here: https://deeplearningcourses.com/course_order Here are the links for my courses: Deep Learning Prerequisites: Linear Regression in Python https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=APR123 Deep Learning Prerequisites: Logistic Regression in Python https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=APR123 Deep Learning in Python https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=APR123 Practical Deep Learning in Theano and TensorFlow https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=APR123 Deep Learning: Convolutional Neural Networks in Python https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=APR123 Unsupervised Deep Learning in Python https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=APR123 Deep Learning: Recurrent Neural Networks in Python https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=APR123 Advanced Natural Language Processing: Deep Learning in Python https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=APR123 Advanced AI: Deep Reinforcement Learning in Python https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=APR123 Easy Natural Language Processing in Python https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=APR123 Cluster Analysis and Unsupervised Machine Learning in Python https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=APR123 Unsupervised Machine Learning: Hidden Markov Models in Python https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=APR123 Data Science: Supervised Machine Learning in Python https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=APR123 Bayesian Machine Learning in Python: A/B Testing https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=APR123 Ensemble Machine Learning in Python: Random Forest and AdaBoost https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=APR123 Artificial Intelligence: Reinforcement Learning in Python https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=APR123 SQL for Newbs and Marketers https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=APR123 And last but not least,$10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning:

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Remember, this post will self-destruct on April 12 (7 days). Act NOW!

New course! Deep Reinforcement Learning in Python

March 27, 2017

Who’s ready for Deep Reinforcement Learning!!!???

Ever since I included this topic in my lecture called “Where does this course fit into my deep learning studies?”, people have been asking me about when my Deep Reinforcement Learning course is coming out.

Well, it’s out right now!

This course continues from where my last course, “Artificial Intelligence: Reinforcement Learning in Python”, left off.

In particular, we are going to be applying different kinds of neural networks to reinforcement learning, and also deepening our knowing of the RL algorithms we already learned about.

Of course, I’m going to link this with an early bird coupon. Get it now before they run out!

https://www.udemy.com/deep-reinforcement-learning-in-python/?couponCode=EARLYBIRDSITE

But… that’s not all.

Today, Udemy has decided to do yet another AMAZING $10 promo. As usual, I’m providing$10 coupons for all my courses in the links below. Please use these links and share them with your friends!

The $10 promo doesn’t come around often, so make sure you pick up everything you are interested in, or could become interested in later this year. The promo goes until the end of the month. Don’t wait! At the end of this newsletter, I’m going to provide you with some additional links to get machine learning prerequisites (calculus, linear algebra, Python, etc…) for$10 too!

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python

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Deep Learning Prerequisites: Logistic Regression in Python

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Deep Learning in Python

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Practical Deep Learning in Theano and TensorFlow

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Deep Learning: Convolutional Neural Networks in Python

https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=MAR456

Unsupervised Deep Learning in Python

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Deep Learning: Recurrent Neural Networks in Python

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Advanced Natural Language Processing: Deep Learning in Python

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Easy Natural Language Processing in Python

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Cluster Analysis and Unsupervised Machine Learning in Python

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Unsupervised Machine Learning: Hidden Markov Models in Python

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Data Science: Supervised Machine Learning in Python

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Bayesian Machine Learning in Python: A/B Testing

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Ensemble Machine Learning in Python: Random Forest and AdaBoost

Artificial Intelligence: Reinforcement Learning in Python

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SQL for Newbs and Marketers

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And last but not least, $10 coupons for some helpful prerequisite courses. You NEED to know this stuff before you study machine learning: General (Site-wide coupon): http://bit.ly/2nYDImE Python http://bit.ly/2nYGMiP Calc 1 http://bit.ly/2nVPjT9 Calc 2 http://bit.ly/2mGwu6t Calc 3 http://bit.ly/2n7LsOg Linalg 1 http://bit.ly/2nlTNQn Linalg 2 http://bit.ly/2nr5Ugy Probability (option 1) http://bit.ly/2nDQENW Probability (option 2) http://bit.ly/2n8HENz Probability (option 3) http://bit.ly/2o7OfJd Probability (option 4) http://bit.ly/2nlYAkz P.S. I’ll be adding content to my Deep RL course in the coming days / weeks. Look for it to increase in length by 25-50%. Go to comments Boston Dynamics – Introducing Handle February 28, 2017 Amazing! #artificial intelligence #boston dynamics #deep learning #reinforcement learning #robots Go to comments New course! Reinforcement Learning in Python January 27, 2017 I would like to announce my latest course – Artificial Intelligence: Reinforcement Learning in Python. This has been one of my most requested topics since I started covering deep learning. This course has been brewing in the background for months. The result: This is my most MASSIVE course yet. Usually, my courses will introduce you to a handful of new algorithms (which is a lot for people to handle already). This course covers SEVENTEEN (17!) new algorithms. This will keep you busy for a LONG time. If you’re used to supervised and unsupervised machine learning, realize this: Reinforcement Learning is a whole new ball game. There are so many new concepts to learn, and so much depth. It’s COMPLETELY different from anything you’ve seen before. That’s why we build everything slowly, from the ground up. There’s tons of new theory, but as you’ve come to expect, anytime we introduce new theory it is accompanied by full code examples. What is Reinforcement Learning? It’s the technology behind self-driving cars, AlphaGo, video game-playing programs, and more. You’ll learn that while deep learning has been very useful for tasks like driving and playing Go, it’s in fact just a small part of the picture. Reinforcement Learning provides the framework that allows deep learning to be useful. Without reinforcement learning, all we have is a basic (albeit very accurate) labeling machine. With Reinforcement Learning, you have intelligence. Reinforcement Learning has even been used to model processes in psychology and neuroscience. It’s truly the closest thing we have to “machine intelligence” and “general AI”. What are you waiting for? Sign up now!! COUPON: https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/?couponCode=EARLYBIRDSITE #artificial intelligence #deep learning #reinforcement learning Go to comments New Years Udemy Coupons! All Udemy Courses only$10

January 1, 2017

Act fast! These $10 Udemy Coupons expire in 10 days. Ensemble Machine Learning: Random Forest and AdaBoost https://www.udemy.com/machine-learning-in-python-random-forest-adaboost/?couponCode=BOXINGDAY Deep Learning Prerequisites: Linear Regression in Python https://www.udemy.com/data-science-linear-regression-in-python/?couponCode=BOXINGDAY Deep Learning Prerequisites: Logistic Regression in Python https://www.udemy.com/data-science-logistic-regression-in-python/?couponCode=BOXINGDAY Deep Learning in Python https://www.udemy.com/data-science-deep-learning-in-python/?couponCode=BOXINGDAY Practical Deep Learning in Theano and TensorFlow https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/?couponCode=BOXINGDAY Deep Learning: Convolutional Neural Networks in Python https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow/?couponCode=BOXINGDAY Unsupervised Deep Learning in Python https://www.udemy.com/unsupervised-deep-learning-in-python/?couponCode=BOXINGDAY Deep Learning: Recurrent Neural Networks in Python https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=BOXINGDAY Advanced Natural Language Processing: Deep Learning in Python https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=BOXINGDAY Easy Natural Language Processing in Python https://www.udemy.com/data-science-natural-language-processing-in-python/?couponCode=BOXINGDAY Cluster Analysis and Unsupervised Machine Learning in Python https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=BOXINGDAY Unsupervised Machine Learning: Hidden Markov Models in Python https://www.udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python/?couponCode=BOXINGDAY Data Science: Supervised Machine Learning in Python https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=BOXINGDAY Bayesian Machine Learning in Python: A/B Testing https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing/?couponCode=BOXINGDAY SQL for Newbs and Marketers https://www.udemy.com/sql-for-marketers-data-analytics-data-science-big-data/?couponCode=BOXINGDAY How to get ANY course on Udemy for$10 (please use my coupons above for my courses):

New course! Ensemble Machine Learning in Python: Random Forest and AdaBoost

December 25, 2016

This course is all about ensemble methods.

We’ve already learned some classic machine learning models like k-nearest neighbor and decision tree. We’ve studied their limitations and drawbacks.

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you’ll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

We’ll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

Announcing Data Science: Supervised Machine Learning in Python (Less Math, More Action!)

September 16, 2016

In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now “machine learning first”, meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

The best part about this course is that it requires WAY less math than my usual courses; just some basic probability and geometry, no calculus!

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.

It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

https://www.udemy.com/data-science-supervised-machine-learning-in-python/?couponCode=EARLYBIRDSITE

UPDATE: New coupon if the above is sold out:

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New course – Natural Language Processing: Deep Learning in Python part 6

August 9, 2016

[Scroll to the bottom for the early bird discount if you already know what this course is about]

In this course we are going to look at advanced NLP using deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

• king – man = queen – woman
• France – Paris = England – London
• December – Novemeber = July – June

We are also going to look at the GLoVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.

Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

See you in class!

https://www.udemy.com/natural-language-processing-with-deep-learning-in-python/?couponCode=EARLYBIRDSITE

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New course – Deep Learning part 5: Recurrent Neural Networks in Python

July 14, 2016

New course out today – Recurrent Neural Networks in Python: Deep Learning part 5.

If you already know what the course is about (recurrent units, GRU, LSTM), grab your 50% OFF coupon and go!:

https://www.udemy.com/deep-learning-recurrent-neural-networks-in-python/?couponCode=WEBSITE

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

Sequences appear everywhere – stock prices, language, credit scoring, and webpage visits.

Recurrent neural networks have a history of being very hard to train. It hasn’t been until recently that we’ve found ways around what is called the vanishing gradient problem, and since then, recurrent neural networks have become one of the most popular methods in deep learning.

If you took my course on Hidden Markov Models, we are going to go through a lot of the same examples in this class, except that our results are going to be a lot better.

Our classification accuracies will increase, and we’ll be able to create vectors of words, or word embeddings, that allow us to visualize how words are related on a graph.

We’ll see some pretty interesting results, like that our neural network seems to have learned that all religions and languages and numbers are related, and that cities and countries have hierarchical relationships.

If you’re interested in discovering how modern deep learning has propelled machine learning and data science to new heights, this course is for you.

I’ll see you in class.