I finally enrolled in Andrew Ng's machine learning course on Coursera. Here is my expectation for this course:

• Get a fun intro to machine learning field.

I studied mathematics, statistics, and took AI course when I was an undergraduate but I never had an intro to ML formally. All the work I have done so far has a very strong relationship with ML but they don't really target on ML specifically. So, I think Andrew Ng's ML course is probably a great intro to this field.

• In preparation for my graduate studies.

I'm thinking of pursuing a research career in NLP and robotics but I need to some ground work to see if they are actually fun like I'm picturing in my mind. In addition, before actually taking graduate level related courses, there might be some gaps I need to fill out. So I think Andrew Ng's course may be a greate bridge course to get me warm up for the serious graduate level ML studies.

• Take a break from Algo studies and keep myself motivated.

I'm currently working on a reading project to finish MAW by the end of this September. The progress so far is on track and I'm having a lot of fun with the book. However, sometimes, I want to experience some different flavor of dishes and take a break. In addition, I'm thinking of the next reading project I'm going to do. It's highly likely going to be a book in linear algebra. I have taken linear algebra before in the college but I found the subject can become quite boring very soon if you don't have specific problems or needs want to address. Hopefully, Andrew Ng's course will help me to find some motivation to study linear algebra well.

• Start to make résumé ML-ish

I'm working in DB field but I always want to do ML by nature judged by my performance in the ML-related courses. Taking ML course on coursera and having a nice badge on my LinkedIn may greatly help me to market my ML expertise in the future?

My current plan is to finish the coursera version first, and then move on to CS229 version if time permits. I want to take an agile approach to this material by doing iteratively build-up.