Lecture #1 History of ML

Lecture #2 How ML works

Lecture #3 Data Splitting, Evaluation & Distance Metrics

Lecture #4 Manifolds, Curse of Dimensionality, KNN & Perceptron

Lecture #5 Perceptron: Hyperplanes, Adjustments

Lecture #6 Margins & The Perceptron

Lecture #7 Maximum Likelihood Estimation

Lecture #8 Estimating Probabilities from Data: Naive Bayes

Lecture #9 Conditional Independence & Multinomial Distribution

Lecture #10 Naive Bayes Classifier: Advanced Concepts

Lecture #11