What material you want to refer to is entirely dependent on What you want to do?. Here are some of my recommendations-
Q : Do you want to have an "Introduction to ML", some applications with Octave/Matlab as your toolbox?
A :Take up Andrew Ng's course on ML in Coursera .
Q : Do you want to have a complete understanding of ML with the mathematics, proofs and build your own algorithms in Octave/Matlab?
A : Take up Andrew Ng's course on ML as taught in Stanford; video lectures are available for free download . Note - This is NOT the same as the Coursera course. For textbook lovers, I have found the handouts distributed in this course far better than textbooks with obscure and esoteric terms. It is entirely self contained. If you want an alternate opinion, try out Yaser Abu-Mostafa's ML course at Caltech .
Q : Do you want to apply ML along with NLP using Python ?
A : Try out Natural Language Tool Kit . The HTML version of the NLTK book is freely available (Jump to Chapter 6 for the ML part) . There is an NLTK cookbook available as well which has simple code examples to get you started .
Q: Do you want to apply standard ML algorithms using Python?
A : Try out scikit-learn . The OP's book also seems to be a good fit in this category (Disclaimer - I haven't read the OP's book and this is not an endorsement).
Not only is the book great, but his lectures are PHENOMENAL. He breaks concepts down in such a careful, accessible way. Its a bit late to join the online course, but you can see all the lectures on YouTube (work.caltech.edu/telecourse.html) or iTunesU (I prefer the latter, using the app on iOS - awesome b/c you can bookmark and record notes at those marks - otherwise I notice these video types of courses are way less useful - no way to review - wish Coursera/Udacity/EdX had that feature.)
Yaser is an awesome guy btw - he's very active on the forum (see the link from the above caltech site - on right hand side). He is very gracious with his time - I'm not a CalTech student, and yet he has answered all my questions and even helped me find a tutor for the course that was a previous student at CalTech (I live in Pasadena). He truly cares - and that comes off in the lectures as well. Enjoy!
Admittedly epub is a better format, because it naturally reflows on smaller screens, but "free" epubs are harder to come across. I've been thinking of converting some really good PDFs that I have, to ePub myself, but just haven't gotten around to it yet.
Also, the voice of some of these lecturers have this sort-of monotone to it, that has the tendency to let you mind wander off. They're just not "arresting" enough.
For instance, I took the Crypto I class part-way on Coursera, and had this experience. The instructor voice was slow, drawn-out and kind-of put you to sleep. I actually downloaded the videos and just played it on VLC at 1.25x or 1.5x the speed (because he spoke so annoyingly slow).
On the other hand Tim Roughegarden (I think that's his name), who teaches an Algorithms class on Coursera, has an amazing "video personality". Just the way he speaks -- it catches your attention. He passion and enthusiasm for the topic really come across. Now, I'm not saying the other professors aren't as passionate about what they teach -- but it's just that some of these lecturers have a really good way of bringing it through (their love for the topic) on video. Not everyone can (or is) doing it.
Unfortunately, I'm not aware of any good ML books that are current. Mitchell's was really good but is out of date. Bishop is a megalithic tome of statistical mathematics and is better as a reference than a textbook. I think that a good MOOC course paired with selected readings is the best currently available option.
This book was recommended to me by a friend (who is a genius and great at ML), and I've just begun reading it.
My problem with MOOC is that I strongly dislike the audio/video format. I love textbooks. I learned a lot of what I know about computer science from books, not lectures. I went through many of Tanenbaum's thoughout high school -- and was more addicted to his textbooks than many of the novels that I read at the time.
I would really like to get some recommendations on some good _textual_ ML material.