Track
ML Basics
Goal: Classic supervised and unsupervised learning.
Prereqs: Python for ML and Math for ML.
Status: done
Work through the steps in order. Bold links open YouTube.
| Step | Concept | YouTube | Read |
|---|---|---|---|
| 1 | What ML is | StatQuest — A Gentle Introduction to Machine Learning | ISLR — Statistical Learning (ch. 2) |
| 2 | Linear regression | StatQuest — Linear Regression | CS229 notes — Linear regression |
| 3 | Logistic regression | StatQuest — Logistic Regression | CS229 notes — Classification and logistic regression |
| 4 | Bias and variance | StatQuest — Bias and Variance | |
| 5 | Cross-validation | StatQuest — Cross Validation | scikit-learn — Cross-validation |
| 6 | Confusion matrix | StatQuest — The Confusion Matrix | |
| 7 | Ridge regularization | StatQuest — Ridge (L2) Regression | scikit-learn — Linear models |
| 8 | Decision trees | StatQuest — Decision Trees | scikit-learn — Decision trees |
| 9 | Random forests | StatQuest — Random Forests Part 1 | scikit-learn — Ensemble methods |
| 10 | Support vector machines | StatQuest — Support Vector Machines | scikit-learn — Support vector machines |
| 11 | K-means clustering | StatQuest — K-means clustering | scikit-learn — Clustering |
| 12 | PCA | StatQuest — Principal Component Analysis |
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