Week 1: Introduction to Machine Learning & AI
- What is ML & how it differs from traditional programming
- Key concepts: training data, labels, predictions
- Types of learning (supervised, unsupervised, reinforcement)
- The ML pipeline & real-world applications
Hands-on: Build a simple regression model
Week 2: Supervised Learning – Regression
- Linear & multiple regression models
- Polynomial regression basics
- Model assumptions & limitations
- Evaluation metrics: MAE, RMSE, R²
Hands-on: Predict housing prices using regression
Week 3: Supervised Learning – Classification
- Logistic regression explained
- k-Nearest Neighbors (kNN) classifier
- Decision Trees basics
- Model evaluation: confusion matrix, precision, recall, F1-score
Hands-on: Build a classifier for customer churn prediction
Week 4: Unsupervised Learning
- Clustering techniques: k-Means & hierarchical clustering
- Dimensionality reduction: PCA basics
- Applications of clustering in business/research
Hands-on: Customer segmentation project
Week 5: Feature Engineering & Data Preparation
- Handling missing data & outliers
- Feature scaling (normalization, standardization)
- Encoding categorical variables
- Feature selection techniques
Hands-on: Prepare a dataset for ML modeling
Week 6: ML Pipelines & Model Validation
- Introduction to scikit-learn pipelines
- Cross-validation & grid search for tuning
- Overfitting vs underfitting
Mini Project: End-to-end ML workflow (EDA → feature engineering → model building → evaluation → reporting)
