A lot of easy to digest content in this! Always great to see quality free material to help more people pick up machine learning and get involved solving problems using data science.<p>One thing I didn't see covered in depth here was feature engineering, which is the process of preparing your raw data for the machine learning algorithms. They cover it briefly in the chapter on "Practical Considerations", but anyone looking to apply ML in the real world should look into feature engineering more on their own.<p>One resource I recommend (and I am biased), is a python library for automated feature engineering called, Featuretools (<a href="https://github.com/featuretools/featuretools/" rel="nofollow">https://github.com/featuretools/featuretools/</a>). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: <a href="https://www.featuretools.com/demos" rel="nofollow">https://www.featuretools.com/demos</a>.
With all of these online courses around, I'm just curious: is there an ML course that teaches you the right model to use? Say for example, the right amount of layers/nodes for a neural net? As a newcomer doing one of these machine learning MOOCs on his free time, it seems to me that it's about chucking in a load of parameters into a black box hoping for the best.
I quickly skimmed a few chapters and like what I see: the language is very friendly and the examples are easy to understand, but there is enough rigor to make this a good introductory book on the subject. Nice!
I have not yet found a course that can cover machine learning and also explains the Maths needed to understand this.This course is no exception. Its extremely difficult for a programmer with no solid footing in Maths to understand it. Any help is a lot appreciated
Wow, this is a lot more in-depth than I was expecting. I started out with the O'Reilly book (Hands-on Machine Learning)[1]. There's some overlap there, but I'd say both this course and the O'Reilly book are well worth your time if you're starting out with machine learning.
[1] <a href="https://amzn.to/2kKaNiQ" rel="nofollow">https://amzn.to/2kKaNiQ</a>
So many ML/AI courses out there, it's amazing.<p>Wondering if those ML/AI specialists can't find themselves a job and start writing courses -.-