Session 1: Introduction to Machine Learning (2 Hours)

• Types of ML (Supervised, Unsupervised, Reinforcement)
• Machine Learning Workflow (Data Collection → Preprocessing → Model Selection → Training → Evaluation)

Session 2: Python for Machine Learning (2 Hours)

• Data Manipulation (NumPy & Pandas)
• Data Visualization (Matplotlib & Seaborn)
• Implementing basic ML algorithms using Scikit-learn

Lab Session: Data preprocessing & visualization using real datasets.

Session 3: Supervised Learning – Regression (3 Hours)

• Linear Regression (Simple & Multiple)
• Model Evaluation Metrics (R², RMSE, MAE)
• Regularization (Ridge, Lasso), Polynomial Regression

Lab Session: Predicting stock prices using regression models.

Session 4: Supervised Learning – Classification (3 Hours)

• Logistic Regression, k-NN, SVM, Decision Trees, Random Forests
• Evaluation Metrics (Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC Curves)

Lab Session: Train & test classification models on real-world datasets.