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Introduction to Machine Learning  

A self-study guide for aspiring machine learning practitioners


Featured on: May 18, 2018

Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. Prerequisites Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites: *Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms. (Familiarity with more advanced math concepts such as logarithms and derivatives is helpful, but not required.) *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. Note: See the Key Concepts and Tools section below for a detailed list of math and programming concepts used in Machine Learning Crash Course, with reference materials for each.


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