Stanford University's machine learning courses, most notably Andrew Ng's Machine Learning course, which covers supervised and unsupervised learning, model evaluation and the underlying theory and practice. It is offered as a set of lecture-based course material.

Commenters point to it as a solid starting point for learning machine learning, with one suggesting people work through its syllabus lesson by lesson and another recommending it after building up linear algebra, probability and statistics.

On Reddit (2)

1. Recommendsdepressed-benchr/ExperiencedDevs2023in “Senior developers are expected to be able to do machine learning”

   Brush up linear algebra, probability and statistics. MIT's ocw has great courses on this; this is foundational knowledge, just like algorithms and DS is for CS. Do the exercises and build some mathematical maturity. Then follow some of Stanford's ML courses.

2. RecommendsKnown-Slice-8698r/learnmachinelearning2022in “[Question] Can I get a proper guide or Roadmap to learn Machine Learning?”▲ 2

   I think you can have a look at this course offered by Stanford University. I usually check the syllabus and take each lesson first. If I wanted to know some more details, I will find other resources or courses about it. To learn machine learning, you may also need to know some statistics basis.

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