Module 4: Supervised Learning Algorithms

• Implementing Linear Regression and Logistic Regression

• Applying k-Nearest Neighbors (k-NN), Decision Trees, Random Forests

• Evaluating models using accuracy, precision, recall, F1-score, and ROC-AUC metrics

Module 5: Unsupervised Learning Algorithms

• Performing K-Means Clustering and Hierarchical Clustering

• Using Principal Component Analysis (PCA) for dimensionality reduction

Capstone Project: Build and Present a Real-World ML Solution

• Select a dataset (e.g., loan prediction, disease detection)

• Conduct EDA and preprocessing

• Train and evaluate ML models

• Present project findings and recommendations

Outcome: Deliver a complete, documented machine learning solution using Python and Scikit-learn.