Session 5: Unsupervised Learning (2 Hours)

• Clustering Algorithms (K-Means, DBSCAN, Agglomerative Clustering)
• Dimensionality Reduction (PCA, t-SNE)

Lab Session: Clustering customer data for market segmentation.

Session 6: Model Evaluation & Hyperparameter Tuning (2 Hours)

• Cross-Validation & Bias-Variance Tradeoff
• Hyperparameter Tuning (Grid Search, Random Search)

Lab Session: Optimizing ML models for higher accuracy.

Session 7: Neural Networks & Deep Learning (3 Hours)

• Introduction to Neural Networks (Perceptrons, Activation Functions)
• Building Deep Learning Models using TensorFlow/Keras
• Applications: CNNs for Image Recognition, RNNs for Time-Series

Lab Session: Implementing a basic neural network.

Session 8: Advanced Topics & Case Studies (2 Hours)

• Reinforcement Learning (Q-Learning, Policy Gradient)
• Real-world case studies (Finance, Healthcare, Retail, E-commerce)