

A New Book: Building Machine Learning Systems with Python - derpapst
http://metarabbit.wordpress.com/2013/05/31/building-machine-learning-systems-with-python/

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denzil_correa
The book details building ML systems with Python and does not necessarily
teach ML per se. It is a good time to write a ML book in Python particularly
keeping in mind efforts to make Python scale to Big Data [0].

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 [1].

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 [2]. 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 [3].

Q : Do you want to apply ML along with NLP using Python ?

A : Try out Natural Language Tool Kit [4]. The HTML version of the NLTK book
is freely available (Jump to Chapter 6 for the ML part) [5]. There is an NLTK
cookbook available as well which has simple code examples to get you started
[6].

Q: Do you want to apply standard ML algorithms using Python?

A : Try out scikit-learn [7]. 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).

[0] [http://www.drdobbs.com/tools/us-defense-agency-feeds-
python/...](http://www.drdobbs.com/tools/us-defense-agency-feeds-
python/240151767)

[1] <https://www.coursera.org/course/ml>

[2] <http://academicearth.org/courses/machine-learning/>

[3] <http://work.caltech.edu/telecourse.html>

[4] <http://nltk.org>

[5] <http://nltk.org/book/>

[6] [http://www.amazon.com/Python-Text-Processing-NLTK-
Cookbook/d...](http://www.amazon.com/Python-Text-Processing-NLTK-
Cookbook/dp/1849513600)

[7] <http://scikit-learn.org>

------
winter_blue
For anyone curious to learn more about machine learning, I would recommend:
[http://www.amazon.com/Machine-Learning-Algorithmic-
Perspecti...](http://www.amazon.com/Machine-Learning-Algorithmic-Perspective-
Recognition/dp/1420067184)

~~~
flatline
I have this book, and would not recommend it as a stand-alone learning guide.
It gives a decent intuitive treatment of some topics but is inconsistent. It
will furthermore jump between e.g. an explanation of neural networks without
any mathematics to the full derivation of backpropagation. It tries to hit a
sweet spot between rigor and intuition but in my opinion it largely fails to
bridge the two.

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.

~~~
gtani
Murphy's is probably most current, and an excellent text (read first review).
OTW there's lots of stuff on the web for various levels of rigor

[http://www.amazon.com/Machine-Learning-Probabilistic-
Perspec...](http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-
ebook/dp/B00AF1AYTQ/)

[http://metaoptimize.com/qa/questions/186/good-freely-
availab...](http://metaoptimize.com/qa/questions/186/good-freely-available-
textbooks-on-machine-learning)

<http://www.p-value.info/2012/11/free-datascience-books.html>

<http://www.kaggle.com/wiki/Tutorials>

~~~
11181514
Now is a good time to pick up the Murphy book:

[http://mitpress.mit.edu/content/spread-knowledge-sale-
detail...](http://mitpress.mit.edu/content/spread-knowledge-sale-details)

<http://mitpress.mit.edu/books/machine-learning-2>

~~~
mdaniel
It appears that the deal doesn't apply to eBooks, as there is no place to
enter the code. Also, they have gigantic red text informing the potential
purchaser that they don't offer Android or "normal" eBooks (PDF, ePub, etc).

