Two strong intro to machine learning courses are Andrew Ng's Machine Learning course from Stanford on Coursera, which you can audit for free, and fast.ai's free Practical Deep Learning. Both teach how models learn from data and how to build basic predictive models in Python. For college credit, a university course modeled on Stanford's CS 229 covers the same ideas with more math and can count toward an AI degree.
Typical topics
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and ensemble methods
- Train/test splits and cross-validation
- Overfitting, bias, and variance
Required courses
- Basic Python programming
- Intro statistics and linear algebra (helpful)
Where to take it
| Provider | Course | Cost |
|---|---|---|
| Coursera / Stanford | Machine Learning (Andrew Ng) | Free audit / paid cert |
| fast.ai | Practical Deep Learning | Free |
| University (for-credit) | CS 229–style ML course | Tuition |