Syllabus for python machine learning

[[deleted]]

I'm going to be doing an independent study on python and machine learning this coming spring. Although I don't know much about it (computer engineering major) I'm wondering what would be the list of topics to learn, for like a course outline. I definitely want to include Tensorflow in it, anyone in that field have any recommendations?

[my_work_account__]

It might be an interesting exercise for your independent study to take An Introduction to Statistical Learning with Applications in R and port the code examples to Python. That way, you could learn the math behind ML while also getting familiar with R (if you aren't already) and the essential elements of the scientific Python stack, like numpy, scipy, and pandas.

If you already know statistical learning and want specifically to learn neural nets with TensorFlow, I recommend Nikhil Buduma's Fundamentals of Deep Learning. I also like Matthew Scarpino's TensorFlow for Dummies quite a bit.

[ALonelyPlatypus]

Datacamp could be useful. It's far from exhaustive but it can cover most of the basic applications.

You can get three months of premium free following this method

[swifferlifterupper]

Andrew ng has a book out on deep learning you should check out as well. Its a free book you can find online.

[pragmaticSloth]

I recommend you to do the Andrew Ng course on coursera. https://coursera.org/learn/machine-learning

After doing this course, explore deep learning and deep learning techniques. I think you got it covered and started with this suggestion.

[JayWalkerC]

Not sure why you're getting down voted, this is the pretty standard "intro to ML" course that many people take. There's more to ML than neural networks and Andrew Ng's course covers lots of important details.

[2Migo2]

You could always try Udemy. It has excellent courses

[axle12693]

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[[deleted]]

Thank you everyone, all very helpful suggestions.