Foundations: Supervised vs Unsupervised learning, train/validation/test split, bias–variance tradeoff, overfitting vs underfitting
Supervised Learning: Linear & Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees, Random Forests, XGBoost, LDA/QDA
Unsupervised Learning: K-Means, DBSCAN, Hierarchical Clustering, PCA, ICA, Anomaly Detection
Model Evaluation: Accuracy, Precision/Recall, F1-score, ROC-AUC, MSE/MAE, Confusion Matrix, Cross-Validation
Model Improvement: Feature engineering, regularization, hyperparameter tuning (Grid/Random Search), handling imbalanced data
Ensemble Methods: Bagging, Boosting, Stacking
Reinforcement Learning: Q-Learning, DQN, Policy Gradients, Actor-Critic
Tools & Libraries: Python, NumPy, Pandas, Scikit-learn, Matplotlib/Seaborn, Jupyter
