COURSE OUTLINE

Day 1: NLP Fundamentals

Focus: Understanding text processing and core NLP concepts

• Introduction to Natural Language Processing (NLP) 
• Real-world applications of NLP (search engines, chatbots, assistants)
• Text preprocessing pipeline overview 
• Tokenization (word-level, sentence-level, subword tokenization) 
• Stop words removal and normalization 
• Stemming vs Lemmatization 
• Introduction to NLP libraries (NLTK, spaCy basics) 
• Hands-on: Preprocessing raw text data using Python

Day 2: Text Analytics and Machine Learning for NLP

Focus: Extracting insights from textual data

• Introduction to text analytics 
• Sentiment analysis concepts and applications 
• Text classification fundamentals 
• Bag of Words (BoW) and TF-IDF feature extraction 
• N-grams and feature representation techniques 
• Building basic NLP machine learning pipelines 
• Hands-on: Sentiment analysis model using real datasets

Day 3: NLP with Deep Learning

Focus: Moving from traditional ML to deep learning models

• Introduction to word embeddings (Word2Vec, GloVe, FastText) 
• Semantic representation of words and context 
• Sequence models overview (RNN, LSTM, GRU) 
• Limitations of traditional sequence models 
• Introduction to Transformer architecture 
• Attention mechanism basics 
• Hands-on: Implementing word embeddings for text data

Day 4: Advanced NLP Applications

Focus: Building intelligent NLP systems

• Named Entity Recognition (NER) and its applications 
• Text summarization techniques (extractive vs abstractive) 
• Question Answering systems overview 
• Chatbot development fundamentals 
• Rule-based vs AI-driven chatbots 
• Introduction to transformer-based NLP models (BERT, GPT overview) 
• Hands-on: Building a simple chatbot or NER application

Day 5: NLP Project Development and Best Practices

Focus: End-to-end NLP implementation and evaluation

• Designing an end-to-end NLP pipeline 
• Data collection and preparation for NLP projects 
• Model evaluation metrics (accuracy, F1-score, precision, recall) 
• Error analysis and model improvement techniques 
• Deployment considerations for NLP applications 
• Best practices for scalable NLP systems 
• Final capstone project development and presentation 
• Project review and feedback session