Yesterday I was asked to suggest some resources to help someone "get up to speed" on Machine Learning. I don't really know much about Machine Learning (ML) but I love to learn new things and figured a bit of research would uncover the basics. And of course as with anything, the more I looked the more I found. So thinking about what the goals might be, I organized some of the resources below and I'd very much like to hear what I'm missing as well as any feedback on resources you found most helpful.
1) Simple online summaries of concepts:
- McKinsey's An Executive's Guide to Machine Learning for a short business overview
- Introduction to Machine Learning Theory and Its Applications provides a nice visual primer
- Machine Learning Basics for a Newbie has a nice comparison list that contrasts ML to commonly confused areas
- Machine Learning Series adds a little more technical perspective on explaining the basics such as problem types
2) Less technical books:
Many of these cover ML topics as part of a broader discussion about data and analytics.
- Big Data, Data Mining and Machine Learning - Value Creation for Business Leaders by Dean
- Data Smart: Using Data Science to Transform Information into Insight by Forman covers many concepts in more technical courses
- Data Science for Business by Provost and Fawcett has been recommended to me before for grounding in the business side of things
- Keeping up with the Quants by Davenport has also been recommended by a colleague Biju Krishnan for all business leaders
3) More technical resources (still for beginners):
The #1 name that comes up over and over again, is Andrew Ng and his Stanford course. He has an online course on the basics of Machine learning that runs from November 30 to the end of February. After reading all the glowing reports on Andrew's material, I'd suggest you check out a few previous class recordings for free to see if his approach is what you're looking for. (By the way, it sounds like Lecture # 2 is pretty good for explaining concepts for non-technical audiences.)
- The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
- A less technical primer to the above is An Introduction to Statistical Learning
- Pattern Recognition and Machine Learning by Bishop
- Bayesian Reasoning and Machine Learning by David Barber
- Machine learning: a Probabilistic Perspective by Kevin Murphy
- Introduction to Machine Learning by Apaydin has come up several times by colleagues such as Sara Gardner
You can image the variety of books and courses of such a big topic. Most recommend getting your feet wet on the basics and then focusing on your areas of interest. You can search for interesting books, videos, and classes but my suggestion would be to review the basics online, seriously consider the Ng course, and check out one from the non-technical list and the last book on the technical list as well.
So what am I missing? What did I get wrong? In particular, what would you suggest for people that are just getting started?