Python Machine Learning

Seems like the kind of stuff you get at by combining sklearn's official docs with some material from one of the moocs (like udacity's machine learning class, coursera's etc.) + some wikipedia. This is my default style of learning: picking a "track" (whatever mooc or a cool side project idea) + lots of small specific resources.

I'm really curious why would you recommend and actual book?

It has all the chances to get outdated in a few months, while the separate resources will get updated for sure. yeah, there's some googling effort, but still...

Free ebooks are every day. Normally it's something older. No Email notifications for the freebies normally. Worth checking up on occasionally though.

They give away one book per day. They don't send an email, but I have a script that scrape their site and send myself an email. I'm happy to add to a mailing list if there's interest.

> Is this part of a Christmas season special, or do they regularly do giveaways?

I specifically remember them doing something like "20 books in 20 days" at the end of the last year, where you could get one book per day.

Seems like that run was quite successful and that they are doing it regularly now. I have like 21 books in my account without actually buying any of them.

EDIT: Correction, I've just checked my order history and it turns out that giveaway I was referring to was at the end of 2014, not 2015.

I've heard good things about two books: this one and one on Postges.

https://news.ycombinator.com/item?id=6933716

Me too. :)

It doesn't sound like it based on the abstract. Machine learning in the Python community tends to favor being approachable.

I don't know anything about this book, but in general the pre-reqs for any form of ML (unless you are working in the abstract or with a "black-box" library or such) are a good working knowledge in statistics/probabilities, and an understanding of linear algebra (mainly how to manipulate and work with vectors/matrices).

Oh - and an understanding of when and how to "parallelize" problems can be important, too.

ML and associated problems aren't the most intuitive to understand, solve, etc - but they are a great challenge to expose yourself to if you have the interest.

/disclosure: I'm currently enrolled in and participating in Udacity's Self-Driving Car Engineer nanodegree, plus I have participated in and completed their CS373 program, and I was a part of (and completed) the original Stanford ML Class MOOC that was taught by Andrew Ng. I guess you can say I am a bit biased on this subject...?

I would recommend knowledge in Python. If you don't know Python prior to reading this book, then you won't learn it here.

The Linear Algebra presented in the book is easy, but if you aren't familiar with Linear algebra then it would be a benefit to learn some basics before reading.

Familiarity with mathematical notation and mathematical reasoning is also a must. If you don't have a basic understanding of being able to follow mathematical arguments, then some side comments (that aren't explicitly stated) may seem mysterious to you.

This book is very suitable for someone with a background in Python and basic Linear Algebra/mathematical reasoning knowledge.

The point of this book is to understand the math/algorithms and not treat the algorithms as blackbox solutions. You'll learn about processing your data, dimension reductions, etc., etc.

You may have to do some outside studying depending on your background, but the author provides those resources for you in the text. Overall this is a very good book and the author did a good job at writing it.