Andrew Ng Machine Learning Alternatives
Hey! So first off let me just say I'm a statistics major that's going into their final year. I've taken all calc's, linear algebra's, intro to stats, probability, regression, etc.
So I've been wanting to get into ML and I know Andrew Ng's ML course is considered the holy grail so I started it. It's been around a week or two and content wise I'm on week 6. I watch the lectures at 2x speed and finish the assignment in Python for each week in like a day or so. Some of the things I learn are new (like the concepts themselves, ie. Gradient descent, Cost functions, NNs, etc) but since I'm so used to working with matrices and math in general, it's not too bad for me.
However at this point I'm getting a little bored. It honestly feels like a stats class for me and I'm at the part where we discuss bias-variance and even though I feel like I've learned a good amount of machine learning, it still feels like I don't know how to apply it to anything at all. Like if I were to do a Kaggle competition right now I would get nowhere.
So I want to switch gears for a bit and do a more applied course. The ML course I know is great and I have around 4 or 6 more weeks to go and I'll get to it eventually, but I really just want to dive into the more applied side of things for now. Like apply the knowledge of what I've learned of NNs into different datasets, etc. Like one goal I have is to implement the NEAT algorithm to a simple game.
Anyway, what do you guys recommend? I've looked around and these are what I've found: Fast.ai, deeplearning.ai, Machine learning A-Z (udemy, I have this for free), Sentdex YouTube series on ML, this course on Coursera by the University of Michigan, or googles crash course.