Day 1: Introduction to NLP and Text Preprocessing

Session 1: Fundamentals of NLP

• Overview of natural language processing and its applications
• Challenges in processing human language

Session 2: Text Preprocessing Techniques

• Tokenization, stemming, lemmatization, and stopword removal
• Hands-on lab: Preprocessing text data for NLP tasks

Session 3: Word Embeddings

• Introduction to word embeddings: Word2Vec and GloVe
• Practical demonstration: Visualizing embeddings with PCA and t-SNE

Day 2: Sequence Modeling with RNNs and LSTMs

Session 1: Recurrent Neural Networks (RNNs)

• Architecture and use cases of RNNs in NLP
• Hands-on lab: Implementing a simple RNN for text generation

Session 2: Long Short-Term Memory Networks (LSTMs)

• Advantages of LSTMs over traditional RNNs
• Hands-on lab: Building an LSTM for sentiment analysis

Session 3: Bidirectional RNNs and Attention Mechanisms

• Exploring advanced sequence modeling techniques
• Practical demonstration: Enhancing LSTM models with attention

Day 3: Transformers and Pretrained Models

Session 1: Introduction to Transformer Architecture

• Key components: Multi-head attention and positional encoding
• Hands-on lab: Understanding the Transformer model

Session 2: Pretrained Language Models

• Overview of BERT, GPT, and other state-of-the-art models
• Practical demonstration: Fine-tuning a pretrained BERT model

Session 3: NLP Applications with Pretrained Models

• Use cases in text classification, named entity recognition, and summarization
• Hands-on lab: Applying pretrained models to real-world tasks

Day 4: Advanced NLP Techniques

Session 1: Sequence-to-Sequence Models and Machine Translation

• Encoder-decoder architecture and applications
• Hands-on lab: Building a machine translation model with Transformers

Session 2: Sentiment Analysis and Text Classification

• Designing models for classification tasks
• Practical demonstration: Evaluating NLP models with accuracy and F1-score

Session 3: Optimizing NLP Models

• Techniques for fine-tuning, hyperparameter optimization, and regularization
• Hands-on lab: Improving model performance with advanced techniques

Day 5: Deployment and Case Studies

Session 1: Deploying NLP Models in Production

• Saving, exporting, and deploying models as APIs
• Hands-on lab: Deploying a sentiment analysis model with Flask or FastAPI

Session 2: Real-World Applications and Case Studies

• Examples from industries such as healthcare, finance, and customer service
• Group discussion: Identifying NLP opportunities in your organization

Session 3: Final Project and Review

• Group activity: Solving a real-world NLP challenge
• Feedback and discussion: Future learning paths in NLP and deep learning