Just Finished Coursera's ML Class | Next Steps
As per one user's great advice from a post about two weeks ago, I began my journey into ML and data science. I completed Andrew Ng's course on ML and found it extremely interesting. I loved every bit of it. I was on coursera every day, and completed everything in that course. It was very cool to go on Kaggle, read some tutorial kernals, and just find myself noting what the provider should have done differently as per Prof. Ng's advice. I feel like I have a solid understanding of the fundamentals of some of the most basic and widely used ML algorithms today, and how to use them properly.
I'd now like to contribute on Kaggle, but I really do not have the skills to do ML (or really any data science) in Python/R. Though I probably could mash up some code from some popular kernals, I really wouldn't know what I was doing, and so that would be pointless. I've discovered two courses (specializations) that focus on deep learning / general data science using Python, that seem pretty good. At this point, I'd like to learn Python over R.
Has anyone taken these courses? Does anyone have an opinion on what are some good ways to learn Python with data science? Sometimes I think I could be overcomplicating this, but I really don't think it's wise to jump into Kaggle, only to possibly burn myself out because I don't know Python. Perhaps someone has been in a similar situation and can help guide me? Again, I could just jump into the above two courses, but if anyone can help optimize my solution so that I start in a better direction, that would be huge!
Thanks you, everyone! As it stands, my game plan is to get on Kaggle, build up a portfolio, and use that to help me land a job in the ML realm. I've actually found some interesting jobs that combine both my collegiate background with ML. Pretty neat.