Recently a few people have asked me for the best courseware for learning machine learning. The truth is there is no simple answer. Certainly the machine learning course by Andrew Ng is a good place to start, but most people I know are looking for more depth. Here are some resources I’ve collected. This list will be expanded and refined over time:

Select online courses for machine learning

Coursera: “Machine Learning” - Andrew Ng (the most popular)

Coursera: “Neural Networks for Machine Learning” - Geoffrey Hinton (for a deep dive)

The are a gazillion other data science / machine learning online courses. Many of them are very short there or only superficially introduce topics and show you barebones implementations. Someone reviewed a large number of them here

Learning Python


Think Python: How to Think Like a Computer Scientist

PHY 546: Python for Scientific Computing - Course taught by Michael Zingale at Stony Brook University, contains many interactive Jupyter notebooks.

Code Like a Pythonista: Idiomatic Python

Learn Python the Hard Way - I used this book somewhat and like how it was faster pace than other books

Introduction to machine learning in Python with scikit-learn (video series)


The Pandas cookbook

Pandas tutorials from Wes McKinney, lead Pandas developer

General programming / algorithms

Problem Solving with Algorithms and Data Structures in Python from Interactive Programming

MIT Introduction to Algorithms  online course

Accessible and information dense research papers

Check out the awesome graphical papers at Distill, such as:

Feature visualization

Cool blog posts, etc

Conv-nets : a Modular Perspective - Christopher Olah

Understanding Convolutions  - Christopher Olah

The Unreasonable Effectiveness of Recursive Neural Networks - Andre Karpathy

Practical seq2seq - Suriyadeepan Ram

Tensorflow Neural Network playground

The Neural Network Zoo

Important papers for deep learning

Attention and Augmented Recurrent Neural Networks

Inceptionism: running “Deep Dream” at Google Research

YouTube channels

Some people love em, some people hate em.

Two minute papers (100,000+ subscribers!)

Siraj Rival - (225,000+ subscribers)


Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron - One of my personal favorite books - if you already know Python well and want to get hands on with machine learning quickly and up to speed on the latest methods & techniques, this is an excellent book. Free draft online, by Ian Goodfellow and Yoshua Bengio and Aaron Courville

“Information Theory, Inference, and Learning Algorithms” by David J.C. MacKay

Kevin Murphy’s Machine learning: a Probabilistic Perspective

Bishop’s Pattern Recognition and Machine Learning

Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning

Kuhn and Johnson, Applied Predictive Modeling

Sebastian Raschka, Python Machine Learning (free, online) Pattern Classification 3rd Edition by R. Duda, P.E. Hart and D.G Stork

Machine Learning: A Bayesian and Optimization Perspective (Net Developers) - by Sergios Theodoridis

David Barber’s Bayesian Reasoning and Machine Learning

Foundations of Machine Learning, Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Learning From Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin - one of the most popular texts, presents a more rigorous statistical learning perspective.

All of Statistics, Larry Wasserman

Probabilistic Graphical Models: Principles and Techniques, Daphne Koller, Nir Friedman

Gaussian Processes For Machine Learning, Carl Edward Rasmussen, Christopher K. I. Williams [free pdf]

Machine Learning with R

Building Machine Learning Systems with Python

Machine Learning with Spark Convex Optimization (9780521833783): Stephen Boyd, Lieven Vandenberghe: Books<

Cool articles for lay audiences

The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe

Books for lay audiences

The Master Algorithm : How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t by Nate Silver

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom (a must read!)

Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

On Intelligence by Jeff Hawkins – highly recommended!!

Select bootcamps:

Insight Data Science Foundation’s Program - 8 weeks, online, paid

Ivy Data Science - variable timeframes, paid, online, onsite

Signal Data Science - Berkeley, pay 20% of salary for one year after you get a job.

The Data Incubator  (NYC, DC, SF) for recent Ph.D.s