Awesome Open Source
Awesome Open Source


A collection of machine learning examples and tutorials.

Find associated tutorials at

Find associated courses at

Please note that not all code from all courses will be found in this repository. Some newer code examples (e.g. most of Tensorflow 2.0) were done in Google Colab. Therefore, you should check the instructions given in the lectures for the course you are taking.

How to I find the code for a particular course?

The code for each course is separated by folder. You can determine which folder corresponds with which course by watching the "Where to get the code" lecture inside the course (usually Lecture 2 or 3).

Remember: one folder = one course.

Why you should not fork this repo

I've noticed that many people have out-of-date forks. Thus, I recommend not forking this repository if you take one of my courses. I am constantly updating my courses, and your fork will soon become out-of-date. You should clone the repository instead to make it easy to get updates (i.e. just "git pull" randomly and frequently).

Where is the code for your latest courses?

Beginning with Tensorflow 2, I started to use Google Colab. For those courses, unless otherwise noted, the code will be on Google Colab. Links to the notebooks are provided in the course. See the lecture "Where to get the code" for further details.

VIP Course Links

Data Science: Transformers for Natural Language Processing

Machine Learning: Natural Language Processing in Python (V2)

Time Series Analysis, Forecasting, and Machine Learning

Financial Engineering and Artificial Intelligence in Python

PyTorch: Deep Learning and Artificial Intelligence

Tensorflow 2.0: Deep Learning and Artificial Intelligence (VIP Version)

Deep Learning Courses Exclusives

Classical Statistical Inference and A/B Testing in Python

Linear Programming for Linear Regression in Python

MATLAB for Students, Engineers, and Professionals in STEM

Other Course Links

Cutting-Edge AI: Deep Reinforcement Learning in Python

Recommender Systems and Deep Learning in Python

Machine Learning and AI: Support Vector Machines in Python

Deep Learning: Advanced Computer Vision

Deep Learning: Advanced NLP and RNNs

Deep Learning: GANs and Variational Autoencoders

Advanced AI: Deep Reinforcement Learning in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Deep Learning: Recurrent Neural Networks in Python

Unsupervised Machine Learning: Hidden Markov Models in Python

Deep Learning Prerequisites: The Numpy Stack in Python

Deep Learning Prerequisites: Linear Regression in Python

Deep Learning Prerequisites: Logistic Regression in Python

Data Science: Deep Learning and Neural Networks in Python

Cluster Analysis and Unsupervised Machine Learning in Python

Data Science: Supervised Machine Learning in Python

Bayesian Machine Learning in Python: A/B Testing

Data Science: Natural Language Processing in Python

Modern Deep Learning in Python

Ensemble Machine Learning in Python: Random Forest and AdaBoost

Deep Learning: Convolutional Neural Networks in Python

Unsupervised Deep Learning in Python

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