Unit 1: Introduction — Types of ML, Bias-Variance Tradeoff, Cross-Validation

Unit 2: Supervised Learning — Linear Regression, Logistic Regression, SVM, Decision Trees

Unit 3: Ensemble Methods — Bagging, Boosting, Random Forests, XGBoost

Unit 4: Unsupervised Learning — K-Means, Hierarchical Clustering, PCA

Unit 5: Neural Networks — Perceptron, Backpropagation, CNN, RNN

Unit 6: Model Evaluation & Deployment