Month 24: Advanced Topics in Machine Learning

Month 24: Advanced Topics in Machine Learning

Week 1: Introduction to Unsupervised Learning

• Day 1: Understanding Unsupervised Learning
• Day 2: Clustering Techniques: K-Means, Hierarchical Clustering
• Day 3: Dimensionality Reduction Techniques: PCA, t-SNE
• Day 4: Association Rule Mining: Apriori, FP-Growth
• Day 5: Anomaly Detection Techniques

Week 2: Introduction to Reinforcement Learning

• Day 1: Understanding Reinforcement Learning
• Day 2: The Elements of Reinforcement Learning: Agent, Environment, Actions, Rewards
• Day 3: Understanding Q-Learning
• Day 4: Deep Q-Networks (DQN)
• Day 5: Policy Gradient Methods

Week 3: Introduction to Natural Language Processing (NLP)

• Day 1: Understanding Natural Language Processing
• Day 2: Text Preprocessing: Tokenization, Stopword Removal, Stemming
• Day 3: Text Vectorization: Bag of Words, TF-IDF
• Day 4: Word Embeddings: Word2Vec, GloVe
• Day 5: NLP Tasks: Text Classification, Sentiment Analysis, Named Entity Recognition

Week 4: Advanced Machine Learning Techniques

• Day 1: Understanding Transfer Learning
• Day 2: Introduction to Semi-Supervised Learning
• Day 3: Introduction to Self-Supervised Learning
• Day 4: Multi-Task Learning and Meta-Learning
• Day 5: Dealing with Imbalanced Data