I think this is fantastic advice.<p>As someone who has spent an embarrassing amount of time on various independent education, one of the key things I've taken from it is just how efficient text books are. Not only can you read more quickly than people can speak, but it's also active by nature. I've often found my attention wandering during videos, but it's just not possible to read without putting in a minimum amount of focus. It's also a lot easier to modulate your reading speed based on how easy material is for you than it is to do the same during a lecture video.<p>Some general thoughts on MOOCs:<p>Coursera and edX tend to be great for small, self-contained topics and the automated graders for programming assignments is great as well. The forums are also useful, though not ideal (since there are no practice questions students can get help with that don't fall under the honor code).<p>Where modern MOOCs really fall down is prerequisites. It's surprisingly difficult to do something like structure an entire CS degree from Coursera classes. Though many classes are taught by famous CS professors, they are from different institutions that break material into courses in different ways. Worse still a lot of the classes are either watered-down or shortened or both.<p>MIT's Open Courseware archives are actually a lot better for this. There are no certificates, and no credentials, but nearly all the material is freely available. The one biggest inefficiency though, is all the time spent in the lectures. At least they can be played back at a higher speed, but the lectures really do take a lot more time and cover less than the textbooks. For courses that have good textbooks, I think the best approach is to skip the lectures except in portions where you feel like you need more review.<p>Finally Khan Academy is fantastic for answering specific, mechanical questions (e.g. how to calculate eigen values), but a bit light on material. I'd use it as a supplement for the other resources.
I love textbooks and spend more of my childrens' inheritance on them than I should.<p>But what MOOCs give me is the <i>exercises</i>. I often think I understand a problem, but it's only after getting 2.1/10 on a Coursera quiz that I realize I've missed a key step or concept.<p>Many textbooks have exercises but few have solutions. I've been working through Barto and Sutton's Reinforcement Learning for example (again), and although I do the exercises and programming questions, I never know if I've gotten it <i>right</i>. My experience with MOOCs shows I probably haven't in a large number of cases.<p>The best of both worlds is when I can follow a MOOC with the textbook to gain more depth, for example with the PGM course on Coursera.
Two free books that I haven't seen mentioned, that are from more of a stats perspective<p>* James, Witten, Hastie, and Tibshirani's <i>An Introduction to Statistical Learning, with Applications in R</i><p><a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow">http://www-bcf.usc.edu/~gareth/ISL/</a><p>* Hastie, Tibshirani, and Freedman's <i>Elements of statistical learning</i> (more advanced)<p><a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow">http://statweb.stanford.edu/~tibs/ElemStatLearn/</a>
PGM is a tough book. I'm not sure it's the right book for "level 3" unless you want to be a level-3 who is good at PGMs.<p>The problem with ML is there are so many different kinds. Bishop's book is a decent light weight survey, but it doesn't come close to covering all the interesting fields. You could read that and Hastie/Tibshirani's book and still know almost nothing about online training (hugely important for "big data" and timeseries), reinforcement learning (mentioned, but not in any depth), agent learning, "compression" sequence predicting techniques, time series oriented techniques (recurrent ANNs for starters, but there is a ton to know here, and most interesting data is time ordered), image recognition tools, conformal prediction, speech recognition tools, ML in the presence of lots of noise, and unsupervised learning. I don't own PGM, but it probably wouldn't help much in these matters either. I know guys who are probably level 4 at machine learning who don't know about most of these subjects. On the other hand, Peter Flach's book "Machine Learning" at least mentions them and makes pointers to other resources.<p>"Deep learning" is becoming kind of a buzzword for a big basket of tricks. I think it's worth knowing about drop-out training, and the tricks used to do semi-supervised learning, but the buzzword is silly. Technically "deep learning" just means "improved gradient descent." I figure level-4 is anyone making progress coming up with new techniques.<p>That said, reading good books is one way to make progress. Knowing the right people is the other way.
PRML is great. I haven't read PGM, but I took a relatively intensive course on it which had great lecture notes. Which I'd like to also suggest—lecture notes are often "skeletal books" which can bring you up to speed on a topic quickly given that you (a) are willing to work a bit more and (b) can fill in the missing fleshy bits with your own experience.<p>I'd also really like to suggest DGL (<a href="http://books.google.com/books/about/A_Probabilistic_Theory_of_Pattern_Recogn.html?id=5uCTngEACAAJ" rel="nofollow">http://books.google.com/books/about/A_Probabilistic_Theory_o...</a>) and Bickel and Doksum (<a href="http://www.amazon.com/Mathematical-Statistics-Basic-Selected-Topics/dp/0132306379" rel="nofollow">http://www.amazon.com/Mathematical-Statistics-Basic-Selected...</a>). These are two of my <i>favorite</i> core ML/stats books.
There are some (free) good books that haven't been mentioned yet:<p>1) "Data Mining and Analysis: Fundamental Concepts and Algorithms" by Zaki and Meira
<a href="http://www.cs.rpi.edu/~zaki/PaperDir/DMABOOK.pdf" rel="nofollow">http://www.cs.rpi.edu/~zaki/PaperDir/DMABOOK.pdf</a><p>This book covers many ML topics with concrete examples.<p>2) "Computer Vision: Models, Learning, and Inference" by Simon Prince: <a href="http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf" rel="nofollow">http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf</a><p>Despite a CV book, the first half of it is like a statistics book that comes with examples in CV which are very easy to follow.
I would also suggest K. Murphy's Machine Learning for the journeyman level. In the intermediate apprentice-journeyman level Alpaydin's Introduction to Machine Learning is very friendly.
Form my own journey I would say that a good place to start for graphical models might be "Bayesian Reasoning and Machine Learning" by Barber. It's free (<a href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online" rel="nofollow">http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...</a>). I haven't read through it, but I've heard good things. However, it doesn't cover some basic things like SVM, RVM, Neural Networks...<p>For those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles and Techniques" by Koller.<p>On the NLP front there's the standard texts "Speech and Language Processing" by Jurafsky and "Foundations of Statistical Natural Language Processing" by Manning.<p>I also like "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani.
Great recommendations, some people might also find this interesting as a general guideline to "Data science" <a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/" rel="nofollow">http://nirvacana.com/thoughts/becoming-a-data-scientist/</a><p>[edit] scroll down and look at the map.
What I'd really appreciate is ideas on how to learn or review the core math concepts. I haven't actually <i>done</i> any multivariable calculus, vector/matrix calculus, or linear algebra in <i>years</i>, even though I took them in undergrad.
Wholeheartily agree with the author's sentiments on the value of textbooks. Not because of the medium itself, but because they're (usually) accompanied by well thoughtout examples and practice problems.<p>When initially starting a dense subject such as PGM, having my hands held through the introductory material with incremental practice problems as the topic elaborated, helped me get a much more intimate grasp. Initially only reading superficially and watching lectures, I kept getting stumped trying to form a cohesive mental map of all the interleaved concepts.
What are the HN community's thoughts on Learning from Data by Abu Mostafa, Magdon-Ismail and Lin (<a href="http://amlbook.com/" rel="nofollow">http://amlbook.com/</a>)? The lectures from their course are here: <a href="http://work.caltech.edu/lectures.html" rel="nofollow">http://work.caltech.edu/lectures.html</a><p>I haven't started it yet, but this book was recommended by some folks at my company.
I'm in the middle of PGM right now. It's actually really easy to follow if you put some time into it. I'm reading PRML next. I didn't realize there was a 'path' to learning ML though, thanks for that.<p>We could use some more ML recs on <a href="https://books.techendo.com/" rel="nofollow">https://books.techendo.com/</a>
Textbooks? Really?<p>How about start with a great lecturer like -<p>Nando de Freitas - <a href="https://www.youtube.com/channel/UC0z_jCi0XWqI8awUuQRFnyw" rel="nofollow">https://www.youtube.com/channel/UC0z_jCi0XWqI8awUuQRFnyw</a><p>David Mackay -<a href="http://videolectures.net/course_information_theory_pattern_recognition/" rel="nofollow">http://videolectures.net/course_information_theory_pattern_r...</a><p>or the (sometimes too dense) Andrew Ng -
<a href="https://www.coursera.org/course/ml" rel="nofollow">https://www.coursera.org/course/ml</a>