HN Books @HNBooksMonth

This is the "online" (abridged) version of this book:

https://www.amazon.com/Bayesian-Methods-Hackers-Probabilisti...

There's a full version of another good book ("Think Bayes: Bayesian Statistics in Python") available as a PDF from the publisher here:

http://greenteapress.com/wp/think-bayes/

(related Amazon reviews)

https://www.amazon.com/Think-Bayes-Bayesian-Statistics-Pytho...

Some good books on Machine Learning:

Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach): http://www.amazon.com/Machine-Learning-Science-Algorithms-Se...

Machine Learning: A Probabilistic Perspective (Murphy): http://www.amazon.com/Machine-Learning-Probabilistic-Perspec...

Pattern Recognition and Machine Learning (Bishop): http://www.amazon.com/Pattern-Recognition-Learning-Informati...

There are some great resources/books for Bayesian statistics and graphical models. I've listed them in (approximate) order of increasing difficulty/mathematical complexity:

Think Bayes (Downey): http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/14493707...

Bayesian Methods for Hackers (Davidson-Pilon et al): https://github.com/CamDavidsonPilon/Probabilistic-Programmin...

Doing Bayesian Data Analysis (Kruschke), aka "the puppy book": http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp...

Bayesian Data Analysis (Gellman): http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-...

Bayesian Reasoning and Machine Learning (Barber): http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Ba...

Probabilistic Graphical Models (Koller et al): https://www.coursera.org/course/pgm http://www.amazon.com/Probabilistic-Graphical-Models-Princip...

If you want a more mathematical/statistical take on Machine Learning, then the two books by Hastie/Tibshirani et al are definitely worth a read (plus, they're free to download from the authors' websites!):

Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/

The Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/

Obviously there is the whole field of "deep learning" as well! A good place to start is with: http://deeplearning.net/

⬐ yedhukrishnan

Those are really useful. Thank you. Books are pricey though!

⬐ shogunmike

I know...some of them are indeed expensive!

At least the latter two ("ISL" and "ESL") are free to download though.

⬐ alexcasalboni

Those are great resources!

In case you are interested in MLaaS (Machine Learning as a Service), you can check these as well:

Amazon Machine Learning: http://aws.amazon.com/machine-learning/ (my review here: http://cloudacademy.com/blog/aws-machine-learning/)

Azure Machine Learning: http://azure.microsoft.com/en-us/services/machine-learning/ (my review here: http://cloudacademy.com/blog/azure-machine-learning/)

Google Prediction API: https://cloud.google.com/prediction/

BigML: https://bigml.com/

Prediction.io: https://prediction.io/

OpenML: http://openml.org/

⬐ yedhukrishnan

I went through the links and your review. They are really good. Thanks!