I'm currently learning and researching more in the field of Machine Learning. I started with Machine Learning course in Coursera (http://ml-class.org/).<p>Any other/more suggestions to go deep into the topic?
Some great resources just mentioned here.<p>If you're interested in Machine Learning and Cloud then you should definitely try AWS ML and Azure ML.<p>"Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology.”<p>"Azure Machine Learning: a cloud-based predictive analytics service."<p>Here two great tutorials (with code) on Amazon ML and Azure ML.<p>Amazon Machine Learning: use cases and a real example in Python <a href="http://cloudacademy.com/blog/aws-machine-learning/" rel="nofollow">http://cloudacademy.com/blog/aws-machine-learning/</a><p>Azure Machine Learning: simplified predictive analytics <a href="http://cloudacademy.com/blog/azure-machine-learning/" rel="nofollow">http://cloudacademy.com/blog/azure-machine-learning/</a>
For a weekly collection of ML related news and resources, you may want to look at <a href="https://aiweekly.curated.co" rel="nofollow">https://aiweekly.curated.co</a>
I would concentrate on just Andrew Ng's course until you finished it. Even though the problem sets are solved using Matlab/Octave you will learn just about all the theory you need to later try different frameworks written in different languages. I earned a 99.6% grade in that class (I have a few decades of AI experience, so I took the class as an excellent review) and I feel that every minute spent on this class was worthwhile.
Checkout, <a href="https://github.com/josephmisiti/awesome-machine-learning" rel="nofollow">https://github.com/josephmisiti/awesome-machine-learning</a><p>This is not exactly resources for learning machine learning but frameworks you can use with your favorite programming language.
Here's my list of suggestions:<p><a href="http://digitalmind.io/post/deep-learning" rel="nofollow">http://digitalmind.io/post/deep-learning</a>
Some good books on Machine Learning:<p>Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach):
<a href="http://www.amazon.com/Machine-Learning-Science-Algorithms-Sense/dp/1107422221/" rel="nofollow">http://www.amazon.com/Machine-Learning-Science-Algorithms-Se...</a><p>Machine Learning: A Probabilistic Perspective (Murphy):
<a href="http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/" rel="nofollow">http://www.amazon.com/Machine-Learning-Probabilistic-Perspec...</a><p>Pattern Recognition and Machine Learning (Bishop):
<a href="http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/" rel="nofollow">http://www.amazon.com/Pattern-Recognition-Learning-Informati...</a><p>There are some great resources/books for Bayesian statistics and graphical models. I've listed them in (approximate) order of increasing difficulty/mathematical complexity:<p>Think Bayes (Downey):
<a href="http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/1449370780/" rel="nofollow">http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/14493707...</a><p>Bayesian Methods for Hackers (Davidson-Pilon et al):
<a href="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers" rel="nofollow">https://github.com/CamDavidsonPilon/Probabilistic-Programmin...</a><p>Doing Bayesian Data Analysis (Kruschke), aka "the puppy book":
<a href="http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884/" rel="nofollow">http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp...</a><p>Bayesian Data Analysis (Gellman):
<a href="http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/" rel="nofollow">http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-...</a><p>Bayesian Reasoning and Machine Learning (Barber):
<a href="http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Barber/dp/0521518148/" rel="nofollow">http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Ba...</a><p>Probabilistic Graphical Models (Koller et al):
<a href="https://www.coursera.org/course/pgm" rel="nofollow">https://www.coursera.org/course/pgm</a>
<a href="http://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193/" rel="nofollow">http://www.amazon.com/Probabilistic-Graphical-Models-Princip...</a><p>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!):<p>Introduction to Statistical Learning:
<a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow">http://www-bcf.usc.edu/~gareth/ISL/</a><p>The Elements of Statistical Learning:
<a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow">http://statweb.stanford.edu/~tibs/ElemStatLearn/</a><p>Obviously there is the whole field of "deep learning" as well! A good place to start is with: <a href="http://deeplearning.net/" rel="nofollow">http://deeplearning.net/</a>