A blog post submitted here mentioned the same sentiment  -
> I can’t fully explain how immensely unmotivating it is to be given a huge list of resources without any context. It’s akin to a teacher handing you a stack of textbooks and saying “read all of these”. I struggled with this approach when I was in school. If I had started learning data science this way, I never would have kept going.
I don't want to host pirated content, so if it is I will remove it.
Also the PDF has a link to a notorious ebook pirate platform on every page. If you really believe content on college pages is legal, you must be very naive. I've never seen a naive webmaster that uses domain privacy though.
What surprised me is that the owner of the Canisius page appears to be teaching staff rather than a student. The other books hosted there seem to be legitimately freely available, however, so I'm guessing that was also a naive mistake.
I'm not very familiar with ebook pirating platforms. So the link didn't seem suspicious to me.
Anyway, the book was removed. Thanks again for pointing it out.
That's not to say this isn't helpful. This is from my own personal experience.
My SO has been trying to learn ML to further her work for a couple months now, and has had a hard time with it. She quite intelligent, but isn't a terribly experienced programmer (she's been writing Matlab for a couple years now, but mostly in a scientific setting)... Either way, I suspect part of the problem is that most of the explanations usually are in a language unfamiliar to her, and expect her to learn or translate it in addition to the concepts.
: Wiki with code, exercises and explanation
: Video lecture one with a recap on back-propagation
: Video lecture two on Sparse Auto Encoders
In terms of books, Bayesian Reasoning and Machine Learning  is Matlab based. So is the Handbook of Monte Carlo methods .
(Hand-crafted by data and code guru James Counts)
While this is no doubt really interesting, I find I am getting diminishing returns from outputting stats like this from big dumps of past historical data. What I would like to be able to show is a live heat graph style stats tracker, where each point in the match updates my belief net about who is winning, or playing better. Of course, the final outcome may be upended by some fluke occurrence such as a Hail Mary pass in the final seconds which is what makes sports interesting, but nonetheless I think a live tracker would say a lot more than the actual score of the match.
So, I am wondering if anyone has specific resources for real time online data mining? At web scale for high throughput data streams. And I agree with shubmajain above, libraries and repos are preferable to books and academic journals ;)
This isn't too far from the logic: "How can we win this game? Score more points than the other team". I suppose the more interesting thing would be to compare the same correlation across players.
I'm actually not sure that the math is true, though. (Or I really don't understand what the stat is saying.) Let's say that it actually is for every 4 serves, you win 3, Djokovic wins 1. That number gives you every game (winning the game game-point-15), to give you every set. I don't see how Djokovic ever wins a game, let alone the set or match.
I would add these great ebooks on Cloud Computing and AWS Certifications:
The Cloud Computing Job Market
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Link  https://cloudacademy.com/ebooks/cloud-computing-job-market-3...
A Guide to AWS Certification Exams
Introduction to the full range of Amazon Web Services certification exams: learn what, why, and how to pass just the right exam for you.
Link  https://cloudacademy.com/ebooks/guide-aws-certification-exam...
AWS Solutions Architect Certification
Study guide to Amazon Web Service's Solutions Architect certification exam: tips and suggestions on how, what, and where to learn.
Link  https://cloudacademy.com/ebooks/aws-solutions-architect-cert...
 As in how you can pick up web hacking, do a few websites and create a reputation and get hired that way without a formal degree.
EDIT: Oops I should have said "An Introduction to Statistical Learning with Applications in R" rather than The Elements of Statistical Learning. The Elements book goes into way too much depth to be a good introduction to the subject.
Although the post gives a link to the Amazon page of the book, PDFs of the chapters are free to download at the official book web site.
It's my opinion that our educational process is a bit too heavy on algorithms and languages while being a bit too light on data structures.
I like to brush up on this subject matter from time to time just to keep myself sharp.
I really enjoyed the book, it took a modern approach to R using many of the newer packages (dplyr for instance) and ggplot and combined them into a very nice introduction to R with labs, etc. Well worth checking out.
Your "smooth-scroll" library is completely breaking my touchpad scroll with an Acer c720 Chromebook. One slight movement (which should be a few pixels scroll) is moving me over half-way down the screen. Makes your site unusable with this touchpad as accidental scrolling sometimes happens and moves the screen a whole page away, especially when trying to right click open links because the gestures are similar.
That said, if I'm being honest, it's fairly unpleasant to use on a desktop with a mouse. It scrolls you to the top after it loads (which is after the rest of the page), and behaves differently than the computer normally does...
I would recommend doing away with it.
You're loading more code just to mess with something that already works without any new benefit (and actually degrading the experience).