Month 4: Machine Learning Fundamentals

Month 4: Machine Learning Fundamentals

Week 1: Supervised Learning

• Day 1: Introduction to Supervised Learning
• Day 2: Classification algorithms: K-Nearest Neighbors, Decision Trees, and Support Vector Machines
• Day 3: Regression algorithms: Linear Regression and Polynomial Regression
• Day 4: Ensemble methods: Random Forests and Gradient Boosting
• Day 5: Practical application of Supervised Learning and a mini-project

Week 2: Unsupervised Learning

• Day 1: Introduction to Unsupervised Learning
• Day 2: Clustering algorithms: K-Means, Hierarchical, and DBSCAN
• Day 3: Dimensionality Reduction: PCA, t-SNE, and LDA
• Day 4: Association Rules: Apriori and Eclat
• Day 5: Practical application of Unsupervised Learning and a mini-project

Week 3: Reinforcement Learning

• Day 1: Introduction to Reinforcement Learning
• Day 2: Understanding the Reward System and Exploration vs Exploitation dilemma
• Day 3: Markov Decision Processes (MDP) and Q-Learning
• Day 4: Deep Reinforcement Learning: Deep Q-Network (DQN)
• Day 5: Practical application of Reinforcement Learning and a mini-project

Week 4: Evaluation Metrics and Model Selection

• Day 1: Introduction to Evaluation Metrics and Model Selection
• Day 2: Metrics for Classification: Accuracy, Precision, Recall, F1-Score, ROC, and AUC
• Day 3: Metrics for Regression: MSE, MAE, RMSE, and R-Squared
• Day 4: Cross-Validation methods and Hyperparameter tuning
• Day 5: Model Selection techniques: Bias-Variance tradeoff, Regularization, and Grid Search