Day 26: Reinforcement Learning
- Concept: Learning through interaction.
- Implementation: Q-learning.
- Evaluation: Reward function, policy.

Day 27: Bayesian Networks
- Concept: Probabilistic graphical models.
- Implementation: Conditional dependencies.
- Evaluation: Inference, learning.

Day 28: Hidden Markov Models (HMM)
- Concept: Time series analysis.
- Implementation: Transition probabilities.
- Evaluation: Viterbi algorithm.

Day 29: Feature Selection Techniques
- Concept: Improving model performance.
- Implementation: Filter, wrapper methods.
- Evaluation: Feature importance.

Day 30: Hyperparameter Optimization
- Concept: Model tuning.
- Implementation: Grid search, random search.
- Evaluation: Cross-validation.

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