

Ask HN: What are some good Machine Learning resources? - yedhukrishnan

I&#x27;m currently learning and researching more in the field of Machine Learning. I started with Machine Learning course in Coursera (http:&#x2F;&#x2F;ml-class.org&#x2F;).<p>Any other&#x2F;more suggestions to go deep into the topic?
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anacleto
Some great resources just mentioned here.

If you're interested in Machine Learning and Cloud then you should definitely
try AWS ML and Azure ML.

"Amazon Machine Learning is a service that makes it easy for developers of all
skill levels to use machine learning technology.”

"Azure Machine Learning: a cloud-based predictive analytics service."

Here two great tutorials (with code) on Amazon ML and Azure ML.

Amazon Machine Learning: use cases and a real example in Python
[http://cloudacademy.com/blog/aws-machine-
learning/](http://cloudacademy.com/blog/aws-machine-learning/)

Azure Machine Learning: simplified predictive analytics
[http://cloudacademy.com/blog/azure-machine-
learning/](http://cloudacademy.com/blog/azure-machine-learning/)

~~~
yedhukrishnan
Thanks! I'll try these out. Can they be used for learning? I feel it's more
like services.

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adorable
For a weekly collection of ML related news and resources, you may want to look
at [https://aiweekly.curated.co](https://aiweekly.curated.co)

~~~
yedhukrishnan
Thanks! This will help me to stay motivated every time when a mail comes :D

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mark_l_watson
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.

~~~
yedhukrishnan
Thanks for the advice. Right now, I am concentrating only on the course. I
just wanted to keep some resources for later.

I'll keep this in mind.

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tacticiankerala
Checkout, [https://github.com/josephmisiti/awesome-machine-
learning](https://github.com/josephmisiti/awesome-machine-learning)

This is not exactly resources for learning machine learning but frameworks you
can use with your favorite programming language.

~~~
yedhukrishnan
That's good. Thanks!

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rayalez
Here's my list of suggestions:

[http://digitalmind.io/post/deep-learning](http://digitalmind.io/post/deep-
learning)

~~~
yedhukrishnan
That's great! Thanks.. Is that your own blog?

~~~
rayalez
Yep =)

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shogunmike
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...](http://www.amazon.com/Machine-Learning-Science-Algorithms-
Sense/dp/1107422221/)

Machine Learning: A Probabilistic Perspective (Murphy):
[http://www.amazon.com/Machine-Learning-Probabilistic-
Perspec...](http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-
Computation/dp/0262018020/)

Pattern Recognition and Machine Learning (Bishop):
[http://www.amazon.com/Pattern-Recognition-Learning-
Informati...](http://www.amazon.com/Pattern-Recognition-Learning-Information-
Statistics/dp/0387310738/)

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...](http://www.amazon.com/Think-Bayes-Allen-B-
Downey/dp/1449370780/)

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

Doing Bayesian Data Analysis (Kruschke), aka "the puppy book":
[http://www.amazon.com/Doing-Bayesian-Data-Analysis-
Second/dp...](http://www.amazon.com/Doing-Bayesian-Data-Analysis-
Second/dp/0124058884/)

Bayesian Data Analysis (Gellman): [http://www.amazon.com/Bayesian-Analysis-
Chapman-Statistical-...](http://www.amazon.com/Bayesian-Analysis-Chapman-
Statistical-Science/dp/1439840954/)

Bayesian Reasoning and Machine Learning (Barber):
[http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-
Ba...](http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-
Barber/dp/0521518148/)

Probabilistic Graphical Models (Koller et al):
[https://www.coursera.org/course/pgm](https://www.coursera.org/course/pgm)
[http://www.amazon.com/Probabilistic-Graphical-Models-
Princip...](http://www.amazon.com/Probabilistic-Graphical-Models-Principles-
Computation/dp/0262013193/)

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/](http://www-bcf.usc.edu/~gareth/ISL/)

The Elements of Statistical Learning:
[http://statweb.stanford.edu/~tibs/ElemStatLearn/](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/](http://deeplearning.net/)

~~~
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/](http://aws.amazon.com/machine-learning/) (my review here:
[http://cloudacademy.com/blog/aws-machine-
learning/](http://cloudacademy.com/blog/aws-machine-learning/))

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

Google Prediction API:
[https://cloud.google.com/prediction/](https://cloud.google.com/prediction/)

BigML: [https://bigml.com/](https://bigml.com/)

Prediction.io: [https://prediction.io/](https://prediction.io/)

OpenML: [http://openml.org/](http://openml.org/)

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

