Lazy Programmer

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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:

Here are the links for my courses:

Deep Learning Prerequisites: Linear Regression in Python

Deep Learning Prerequisites: Logistic Regression in Python

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

Deep Learning: Convolutional Neural Networks in Python

Unsupervised Deep Learning in Python

Deep Learning: Recurrent Neural Networks in Python

Advanced Natural Language Processing: Deep Learning in Python

Advanced AI: Deep Reinforcement Learning in Python

Easy Natural Language Processing in Python

Cluster Analysis and Unsupervised Machine Learning in Python

Unsupervised Machine Learning: Hidden Markov Models in Python

Data Science: Supervised Machine Learning in Python

Bayesian Machine Learning in Python: A/B Testing

Ensemble Machine Learning in Python: Random Forest and AdaBoost

Artificial Intelligence: Reinforcement Learning in Python

SQL for Newbs and Marketers

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):
Calc 1
Calc 2
Calc 3
Linalg 1
Linalg 2
Probability (option 1)
Probability (option 2)
Probability (option 3)
Probability (option 4)

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