Lecture 1 | Machine Learning (Stanford)

I second the nomination of Bishop. It is the standard text. It is only two years old, and Bishop will teach you machine learning the way that the field practices it nowadays. In my lab of fourteen people, we must have six or so copies of Bishop. I don't understand what is impractical about Bishop. If you are looking blindly to use an off-the-shelf machine learning implementation, that's one thing. Machine Learning has been described as the study of bias. If you want to understand when to pick certain techniques, and develop appropriate biases, then read Bishop. "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman gives more of a statistician's approach. The treatment is simply less broad, and also more dated. You can also look at Andrew Ng's video lectures: [this video] He is very well-respected in the field. For certain students, watching a lecture may be preferable to reading a book.

Machine Learning: * [this video] : Andrew Ng's machine Learning course, the recommended entry point by most people * [link] : More kaggle focused, but also more modern and has interesting projects Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects Deep Learning * [link] - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders" * [link] : More bottom-up approach, helps to understand the theory better Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects. After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts ( [link] )

First, finish lectures by Professor Gilbert Strang. [link] To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. [link] [this video] If you just want to use the libraries, you can stop here. If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. [link] Then, you can try to finish EE364 and watch the videos. [link] [link] If you want to roll your own algorithms, you have to know some optimization tools. [link] And there is some statistics knowledge you have to fill in. I used these: [link] [link] R is used in the courses.