Yeah, I have to admit: I love Google. :D For many reasons (that are yet to come in this Machine Learning Starter Kit). One of them is Google’s most recent initiative, the Machine Learning Crash Course, which allows us to learn about real-world case studies from some of the best researchers in industry. It comprises of 25 lessons and more than 40 hands-on exercises. In the course of my own learning process I came across dozens of Machine Learning tutorials and online courses. They all have their strengths and weaknesses. Some are very practical, giving you a bunch of templates to work with, but no in-depth explanations on models, code and math. Some are theory-oriented, emphasizing a strong mathematical foundation. Compared, I liked the Google Crash Course a lot, it was probably one of the best and def worth checking out.
Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, Google recommends that students meet the following prerequisites:
Intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms.
Proficiency in programming basics, and some experience coding in Python. Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.
Ready? Let’s get started. :D
As always, have fun and let me know what you think!