Khan Academy looks like a good beginning for linear algebra:
MIT 6.041SC seems like a good beginning for probability theory:
Then, for machine learning itself, pretty much everyone agrees that Andrew Ng's class on Coursera is a good introduction:
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:
This online book is a very good resource to gain intuitive and practical knowledge about neural networks and deep learning:
Finally, I think it's very beneficial to spend time on probabilistic graphical models. Here is a good resource: