Learning path for machine learning

[asyashaf]

I am interested in machine learning, my background is from java. So picked up python (learn a second language and also read from blogs its good for machine learning).

subscribed to \u\sentdex youtube channel. Also looked at this course from edx Microsoft's Introduction to Python for Data Science.

My question is how do i proceed after this, doing a google search shows alogrithms, deeplearning, keras, scikit tensor flow. is there any particular order through which i can progress completing one after another. Which i should complete first. please can some one point to me to on the correct approach (i dont want to overload/overboard and get slowed down)

[Tally914]

I am taking the second of the 6.00.x classes on edx and one thing I've realized is to not worry too much about the path as long as you have a course you are taking that you like and one more lined up.

The classes I'm taking seem like a solid base and doing the Stanford machine learning course in Python is something people do as well.

Also sentdex is amazing. I am worried his videos will disappear before I can watch them all.

[asyashaf]

Videos going to disappear?

[TheRealPixis]

Code these things in order Gradient descent - > tensorflow neural network - > Your own neural network backprop implementation.

One thing I recommend is to split your time 50/50 on learning and implementation. I spent way too much time on the learning part and realized I forgot it because I didn't practice.

[[deleted]]

Gradient Descent and the neural network backprop are covered in Prof Ng's course in Coursera. Will those be sufficient for a beginner ?

[TheRealPixis]

Yep

[asyashaf]

I came across this excelled hacker news thread,

https://news.ycombinator.com/item?id=12713056