Introduction-Types of Machine Learning-Difference between Traditional Vs Machine Learning approach-Applications of ML-ML Pipeline-Problem formulation, data collection, preprocessing, modeling, evaluation, deployment-Key ML Concepts-Dataset types -structured vs unstructured-Train/Test split, Features & Labels Ethical considerations: Bias, fairness, transparency in ML

Regression-Linear Regression-Polynomial Regression-Evaluation: MAE, MSE, R² score-Classification-Logistic Regression-k-Nearest Neighbors-Decision Trees-Naïve Bayes-Overfitting, Underfitting-Cross-Validation-Bias– Variance Trade off

Support Vector Machines-Ensemble Methods-Bagging, Random Forest-Boosting-Feature Selection & Dimensionality Reduction-PCA, LDA-Model Interpretability

Clustering-k-Means-Hierarchical Clustering-DBSCAN-Association Rule Mining-Apriori Algorithm-Anomaly Detection-Evaluation Metrics for Clustering

Basics of RL – RL Framework – Markov Decision Process(MDP) – Exploration Vs Exploitation – Polices -Value Functions and Bellman Equations – Solution Methods – Q-learning - Case Study: Smart Traffic Signal Control