I think the data scientist hiring frenzy may soon collapse as large companies run out of patience with their recently formed data science departments that struggle to deliver ROI (for many reasons, often not the fault of the data scientists). But I think we are not yet at the final iteration of the job market for these sorts of skills. Companies usually don't really want someone who specializes in model tuning and algorithm creation, they want something like a "full-stack data analyst" - someone who acknowledges that the modeling may be 2% of the effort and the rest is business analysis, data wrangling, engineering, stakeholder management, building tools for users/operators, etc., and rolls up their sleeves to deliver an end-to-end solution. There does not yet exist a catchy name for this role, but I bet that in a few years it will be what everyone wants to hire. So skate to where the puck will be...

Ian Goodfellow’s Deep Learning book pretty much useless. I own it and have read through most parts of it. I couldn’t explain it better than top Amazon reviews:

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

And I’m surprised to not find Aurelion Geron’s absolute masterpiece listed below. I believe it is the best machine learning book ever, although Statistical Learning mentioned in the article is really good as well :

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

I really recommend Murphy’s “Machine Learning: a probabilistic perspective”. Murphy’s lays the groundwork for understanding how the algorithms work, why and how they could be adapted to the problem you’re dealing with. It takes you from complete beginner (with a reasonable math level) to one step above `import sklearn as sk`.

The other books I read make the field look like a bunch of heuristics that just happen to work.

Since the site mentions "An Introduction to Statistical Learning":

The first book on statistical learning by Hastie, Tibshirani and Friedman, which is absolutely terrific, is freely available for download:

The Elements of Statistical Learning

http://web.stanford.edu/~hastie/ElemStatLearn/

Lists like this are awesome, but I can’t help but think we need some sort of lists tool that lets people create them, others vote on them reddit style, leave comments, rate each item, etc. almost like a subreddit type thing per list, maybe without the temporal decay component of the algorithm.

Then we could have “10 best intro to machine learning resources” as a living breathing list.

The Introduction to Statistical Learning book is great.

But, and I think this is not stated enough, there is a big difference between statistical learning and machine learning in terms of how you approach a problem. The subject matter might be same, but the approach to solve problem is different, one is a 'statistics' approach, one is a 'CS' approach. Depending on your background, you might like one but not the other.

You can know more of what I am talking about by reading this famous piece from Leo Breiman [0].

Personally, I feel I was fortunate enough to learn ML from a so called 'CS' perspective through Andrew Ng's course on Coursera.

0. https://projecteuclid.org/download/pdf_1/euclid.ss/100921372...

I wouldn't say these are level-up but rather some introductory material that covers the basics.

Swapping Introduction to Statistical Learning for Elements of Statistical Learning is a good step-up if you don't need as much hand-holding (it's essentially the same book, by the same author just more thorough). Then, adding Bishop's ML book is a good idea. Although also introductory, it covers a lot more topics (some kernel methods and probabilistic stuff) and in a more disciplined way.

Also, while not that popular in the deep learning hype era, Vapnik's Nature of Statistical Learning is a great read.

I find watching all of the machine learning courses that are posted to YouTube to be a good way to keep up and to get insight into the thinking of the authors of recent papers. It has more or less become my morning ritual to watch one lecture a day.

That said, the past few weeks have been an absolute tsunami of potentially groundbreaking papers. And it is hard to keep up with The cutting edge.

One god-fatherly advice: you won't get a data science job by just reading these three books. You need to work hard and do other things too. Like working on many projects.