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Gain background knowledge first, it will make your life much easier. It will also make the difference between just running black box libraries and understanding what's happening. Make sure you're comfortable with linear algebra (matrix manipulation) and probability theory. You don't need advanced probability theory, but you should be comfortable with the notions of discrete and continuous random variables and probability distributions.

Khan Academy looks like a good beginning for linear algebra: https://www.khanacademy.org/math/linear-algebra

MIT 6.041SC seems like a good beginning for probability theory: https://www.youtube.com/playlist?list=PLUl4u3cNGP60A3XMwZ5se...

Then, for machine learning itself, pretty much everyone agrees that Andrew Ng's class on Coursera is a good introduction: https://www.coursera.org/learn/machine-learning

If you like books, "Pattern Recognition and Machine Learning" by Chris Bishop is an excellent reference of "traditional" machine learning (i.e., without deep learning).

"Machine Learning: a Probabilistic Perspective" book by Kevin Murphy is also an excellent (and heavy) book: https://www.cs.ubc.ca/~murphyk/MLbook/

This online book is a very good resource to gain intuitive and practical knowledge about neural networks and deep learning: http://neuralnetworksanddeeplearning.com/

Finally, I think it's very beneficial to spend time on probabilistic graphical models. Here is a good resource: https://www.coursera.org/learn/probabilistic-graphical-model...

Have fun!




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