"Artificial Intelligence, Deep Learning, Machine Learning — whatever you are doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years." --- Mark Cuban
Machine Learning
Technical: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Non-Technical: The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Well structured MOOC courses.
Having done a few certified courses, from a former Chief Scientist at Baidu and professor at Stanford University, I would like to share with you my experience.
Andrew Ng, Machine Learning : This course provides one of the best resource for a beginner to get an hands-on experience with all the terminologies related to AI and ML. You do not need to know the math for this course. Course assignments are challenging. Language used for this course is Matlab.
Machine Learning A-Ztm : This course provides best practical implementation of various ML algorithms in Python and R. It has an exhaustive list of algorithms. It focuses more on practical stuffs rather than theory. It has amazing datasets as well. Its very helpful for beginner.
Deep Learning Specialisation : Probably the best resource for Deep Learning. It has a series of five courses bundled together and has a very structured approach to learn Deep Learning.
Build Kaggle Profile, Compete.
The best way to learn ML and Data Science is to start applying it to real world problems. Kaggle is a platform where people compete and solve real world problems pertaining to Data Science.