Month 6: Natural Language Processing (NLP)
Week 1: Text Processing and Feature Extraction
- Day 1: Introduction to Natural Language Processing (NLP)
- Day 2: Text Processing: Tokenization, Stemming, Lemmatization
- Day 3: Text Feature Extraction: Bag of Words, TF-IDF
- Day 4: Word Embeddings: Word2Vec, GloVe
- Day 5: Practical application and a mini-project
Week 2: Text Classification and Sentiment Analysis
- Day 1: Introduction to Text Classification and Sentiment Analysis
- Day 2: Naive Bayes Classifier for Text Classification
- Day 3: Using Machine Learning Algorithms for Text Classification (SVM, Random Forest)
- Day 4: Sentiment Analysis: Lexicon-based approach, Machine Learning approach
- Day 5: Practical application and a mini-project
Week 3: Sequence Models and Attention Mechanisms
- Day 1: Sequence Models: Introduction to RNNs, LSTMs, GRUs
- Day 2: Bidirectional and Deep RNNs for sequence prediction
- Day 3: Introduction to Attention Mechanisms
- Day 4: Implementing Attention in sequence models
- Day 5: Practical application and a mini-project
Week 4: Transformers and Pretrained Language Models (BERT, GPT, etc.)
- Day 1: Introduction to Transformers and the Transformer Architecture
- Day 2: Understanding Self-Attention and Positional Encoding
- Day 3: Introduction to BERT: Architecture, Training, and Applications
- Day 4: Introduction to GPT: Architecture, Training, and Applications
- Day 5: Practical application and a mini-project
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