Full Curriculum
13 topics. No gaps. Basic math to cutting-edge research.
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01
What is Machine Learning?
Supervised, unsupervised, reinforcement, when ML works and when it doesn't
4/4
02
Data for ML
What kinds of data ML uses and why splitting matters
2/2
03
Supervised Learning
Regression, classification, trees, intuition not derivation
4/4
04
Evaluation & Model Selection
Why accuracy misleads and how to choose the right model
3/3