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)
Lectures for course on reinforcement learning.
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
PHY 546: Python for Scientific Computing - Course taught by Michael Zingale at Stony Brook University, contains many interactive Jupyter notebooks.
Learn Python the Hard Way - I used this book somewhat and like how it was faster pace than other books
General programming / algorithms
Problem Solving with Algorithms and Data Structures in Python from Interactive Programming
Accessible and information dense research papers
Check out the awesome graphical papers at Distill, such as:
Cool blog posts
Conv-nets : a Modular Perspective - Christopher Olah
Understanding Convolutions - Christopher Olah
The Unreasonable Effectiveness of Recursive Neural Networks - Andre Karpathy
Practical seq2seq - Suriyadeepan Ram
Important papers for deep learning
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.
http://www.deeplearningbook.org/ 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
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]
Cool articles for lay audiences
Books for lay audiences
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!!
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