Month 10: Natural Language Processing (NLP)

Week 1: Introduction to NLP

• Recap of Machine Learning and Deep Learning Fundamentals
• Overview of NLP tasks and applications
• Challenges in NLP

Week 2: Text Preprocessing and Representation

• Text Preprocessing Techniques
• Feature Extraction
• Text Vectorization (Bag-of-Words, TF-IDF)
• Word Embeddings (Word2Vec, GloVe)

Week 3: NLP Models and Techniques

• Naive Bayes
• Maximum Entropy
• Hidden Markov Models (HMMs)
• Conditional Random Fields (CRFs)

Week 4: Deep Learning for NLP

• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) Networks
• Gated Recurrent Units (GRUs)
• Convolutional Neural Networks (CNNs)