Amy Hodler

Machine Learning: How You Get Started

Blog Post created by Amy Hodler Employee on Nov 17, 2015

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:

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2) Less technical books:

Many of these cover ML topics as part of a broader discussion about data and analytics.


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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.)


51oSA8-QHEL._SX440_BO1,204,203,200_.jpgThe above is recommended by many as a way to get started on the technical concepts with the below books being the next step to cover more breadth:

 

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?

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