Is this part of a Christmas season special, or do they regularly do giveaways? And do they send emails about the free book of the day?
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It has all the chances to get outdated in a few months, while the separate resources will get updated for sure.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...
Thus far Udacity's course fits your explanation of using sklearn docs, tweaking inputs, etc. while PML provides a technical explanation of weighting with perceptrons and then you hand-write a perceptron learning algorithm.
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.
Does anyone have any resources to get into ML without having to understand a lot of maths? I just want to understand the concepts, the pros/cons of the different algorithms, and how to use them with some common libraries/services that exist today (TensorFlow?).
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.
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...?