When applied and optimized for real-world problems, Machine Learning can prove to be very useful. I found some of the resources below helpful.
- Kaggle Machine Learning Micro Courses: A perfect Introductory set of courses that directly introduce you to practical applications with just enough theory. Ranging from Basic ML topics to Advanced ones like Neural networks and Reinforcement Learning.
Google’s Machine Learning Crash Course: A perfect short and practical introduction to Machine learning. An excellent supplementary reading and practice session for the Kaggle Micro courses.
- The two above courses should be good. Practical Deep Learning: I haven’t used it, but it comes highly recommended. Some of the content is advanced, and some background in ML is helpful but not necessary. Don’t worry about this unless you have lots of free time.
The only subdomain of ML that sparks my interest in NLP. NLP Kaggle Notebooks Part1, Part 2 and Part 3: A very nicely explained Kaggle notebook that takes an easy and practical approach to teach Natural language processing. And hence this notebook finds a place here.
Spacy NLP: An Industrial strength python NLP library.
Udacity Free ML Course: An old course with a good mix of practical and theory to get you a good foundation in Machine Learning.