This post lists relevant learning materials for those that wish to advance their knowledge in the field of machine learning.
Machine Learning
Format: Coursera course
Presenter: Andrew Ng
Cost: Free
Suggested Audience: Beginners (especially those with a preference for Matplotlib)
A free and well-taught introduction from Andrew Ng, one of the most influential figures in this field. This course has become a virtual rite of passage for anyone interested in machine learning.
Format: Online blog tutorial
Author: EECS Berkeley
Suggested Audience: Upper intermediate to advanced
A practical demonstration of reinforcement learning, and Q-learning specifically, explained through Pac-Man.
Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners
Format: E-book
Author: Scott Hartshorn
Suggested Audience: Established beginners
A short, affordable (USD $3.20), and engaging read on decision trees and random forests with detailed visual examples, useful practical tips, and clear instructions.
Format: E-book
Author: Scott Hartshorn
Suggested Audience: All
A well-explained and affordable (USD $3.20) introduction to linear regression, as well as correlation.
The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future
Format: E-Book, Book, Audiobook
Author: Kevin Kelly
Suggested Audience: All (with an interest in the future)
A well-researched look into the future with a major focus on AI and machine learning by The New York Times Best Seller Kevin Kelly. Provides a guide to twelve technological imperatives that will shape the next thirty years.
Format: E-Book, Book, Audiobook
Author: Yuval Noah Harari
Suggested Audience: All (with an interest in the future)
As a follow-up title to the success of Sapiens: A Brief History of Mankind, Yuval Noah Harari examines the possibilities of the future with notable sections examining machine consciousness, applications in AI, and the immense power of data and algorithms.
Learning Python, 5th Edition
Format: E-Book, Book
Author: Mark Lutz
Suggested Audience: All (with an interest in learning Python)
A comprehensive introduction to Python published by O’Reilly Media.
Format: E-Book, Book
Author: Aurélien Géron
Suggested Audience: All (with an interest in programming in Python, Scikit-Learn and TensorFlow)
As a highly popular O’Reilly Media book written by machine learning consultant Aurélien Géron, this is an excellent advanced resource for anyone with a solid foundation of machine learning and computer programming.
The Netflix Prize and Production Machine Learning Systems: An Insider Look
Format: Blog
Author: Mathworks
Suggested Audience: All
A very interesting blog demonstrating how Netflix applies machine learning to form movie recommendations.
Format: Coursera course
Presenter: The University of Minnesota
Cost: Free 7-day trial or included with $49 USD Coursera subscription
Suggested Audience: All
Provided by the University of Minnesota, this Coursera specialization covers fundamental recommender system techniques including content-based and collaborative filtering as well as non-personalized and project-association recommender systems.
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Deep Learning Simplified
Format: Blog
Channel: DeepLearning.TV
Suggested Audience: All
A quick video series to get you up to speed with deep learning. Available for free on YouTube.
Format: Coursera course
Presenter: deeplearning.ai and NVIDIA
Cost: Free 7-day trial or included with $49 USD Coursera subscription
Suggested Audience: Intermediate to advanced (with experience in Python)
A robust curriculum for those wishing to learn how to build neural networks in Python and TensorFlow, as well as career advice, and how deep learning theory applies to industry.
Format: Udacity course
Presenter: Udacity
Cost: $599 USD
Suggested Audience: Upper beginner to advanced, with basic experience in Python
Comprehensive and practical introduction to convolutional neural networks, recurrent neural networks, and deep reinforcement learning taught online over a four-month period. Practical components include building a dog breed classifier, generating TV scripts, generating faces, and teaching a quadcopter how to fly.
Will a Robot Take My Job?
Format: Online article
Author: The BBC
Suggested Audience: All
Check how safe your job is in the AI era leading up to the year 2035.
Format: Blog
Author: Todd Wasserman.
Suggested Audience: All
Excellent insight into becoming a data scientist.
Format: Blog
Author: Drew Conway
Suggested Audience: Al
The popular 2010 data science diagram designed by Drew Conway.
