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Trying to help answer the question "How do I learn about ML?"

Learning Machine Learning

I often get asked "How do I learn about ML?". There are lots of good answers. I do have a preferred answer that is offered here. I believe in a broad understanding of the topic, terminology, problem framing and method selection while also building up fundamental understandings of how everything actually works.

  1. Good overview with terminology, methods, problem framing, and tips for using APIs from TensorFlow. The Machine Learning Crash Course
  2. Strong Introduction to Fundamentals with Andrew Ng offered by Stanford on Coursera. Machine Learning
  3. Pull it all together with excellent walk-throughs of the core concepts from the Youtube channel 3Blue1Brown
    1. Neural Networks playlist
    2. Essence of linear algebra playlist
    3. Essence of calculus playlist

This is a good review order: 1, 3.1, 3.2, 3.3, 2, 3.1 (again!). When done with this, remember you are a beginner so begin, have fun, make mistakes and keep optimizing your cost function to boost your knowledge!

A good next step is to use the curriculums curated by the Tensorflow community. These are great at balancing coding, math & stats, theory, and project based learning.

My Attempt!

I like to teach ML with a bit of statistics. Here I will uncover the journey through statistics and into machine learning using an essential technique: regression.

Plan: migrate and expand content at this repository.

Certification

The official Google Cloud Professional Machine Learning Engineer certification:

  • The Learning Path contains a series of courses and labs that are excellent