Trying to help answer the question "How do I learn about ML?"
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.
- Good overview with terminology, methods, problem framing, and tips for using APIs from TensorFlow. The Machine Learning Crash Course
- Strong Introduction to Fundamentals with Andrew Ng offered by Stanford on Coursera. Machine Learning
- Pull it all together with excellent walk-throughs of the core concepts from the Youtube channel 3Blue1Brown
- Neural Networks playlist
- Essence of linear algebra playlist
- 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.
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.
The official Google Cloud Professional Machine Learning Engineer certification:
- The Learning Path contains a series of courses and labs that are excellent