[nobullet]

There was HN thread about this: https://news.ycombinator.com/item?id=11859165

Below is my favorite response by vaibkv:

vaibkv 15 days ago

Here's a tentative plan- 1. Do fully AndrewNg's course from Coursera 2. Do a course called AnalyticsEdge by MIT folks from edx.org. I can't recommend this course highly enough. It's a gem. You will learn practical stuff like RoC curves, and what not. Note that for a few things you will need to google and read on your own as the course might just give you an overview. 3. Keep the book "Elements of Statistical Learning" by Trevor Hastie handy. You will need to refer this book a lot. 4. There is also a course that Professor Hastie runs but I don't know the link for it. I highly recommend it as it gives a very good grounding on things like GBM, which are used a lot in practical scenarios. 5. Pick up twitter/enron emails/product reviews datasets and do sentiment analysis on it. 6. Pick up a lot of documents on some topic and make a program for automatically producing a summary of those documents - first read some papers on it. 7. Don't do Kaggle. It's something you do when you have considerable expertise with ML/AI. 8. Pick up flights data and do prediction for flight delays. Use different algorithms, compare them. 9. Make a recommendation system to recommend books/music/movies (or all). 10. Make a Neural Network to predict moves in a tic-tac-toe game. These are a few things that can get you started. This is vast field but once you've done the above in earnest I think you have a good grounding. Pick a topic that interests you and write a paper on it - it's not such a big deal.