16 Recommended Study Resources for Machine Learning
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.
Project 3: Reinforcement 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.
| Basic Algorithms |
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.
Linear Regression And Correlation: A Beginner’s Guide
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 Future of AI |
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.
Homo Deus: A Brief History of Tomorrow
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.
| Programming |
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.
| Recommendation Systems |
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 |
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.
Deep Learning Specialization: Master Deep Learning, and Break into AI
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.
| Future Careers |
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.
So You Wanna Be a Data Scientist? A Guide to 2015’s Hottest Profession
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.