If you are planning to take it, I would suggest a different approach which I have started.

1. Do Practical Deep Learning For Coders:-

http://course.fast.ai/ To take a plunge directly into deep learning AI, this course has rave reviews. This course will allow you to do practical industry level stuff first, then learn theory behind it, rather than other way.

Course description:

>This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

2. Read http://www.deeplearningbook.org/ to gain the relevant math behind it. If you don't know some of the math like calculus or linear algebra presented in the book, learn as you read it from sources like Khan academy.

Now we are up to date on practical side of things, especially deep learning part. We can move on to gain a more generalized and rigorous outlook on various machine learning techniques.

3. Do https://see.stanford.edu/Course/CS229/ - CS229 By Andrew NG , its more rigorous, and complete compared to coursera course. And coursera course is not

I think within a year (max) just this coursework plan would give a strong foundations on practical, theoretical side of things in AI.

I get distracted trying to learn so many stuff at once, (clojure , sicp, haskell, advanced algo) etc etc. So I made this lesson plan to follow as I am interested in AI the most.

The fundamental techniques of machine learning will always be there. For that reason, Ng's ML course will always be the prime entry point for people new to ML. As someone who has taken both the Ng course and has roughly watched the videos of the fast ai course and done some work, I would recommend first doing the Ng course. Things will be much more calm if you then approach the fast ai DL course. The fast ai course is amazing and hats off to Jeremy and Rachel for the great work. But without any idea of machine learning, I would say you will need to put at least 20 hours of work each week to work through it, and that is if you are also a really proficient programmer.

Yes. It may not cover some of the most recent advances (e.g. deep learning) but it lays the foundations required to understand them. Besides, it is really really accessible. And fun. I enjoyed taking the class.

Yes his course is good. Not sure if up to date. But worth doing. I am doing it now.

It's a good foundations course. Read Ian Goodfellows Deep Learning book for a more modern and extensive treatment.