Day 1
Session 1: Introduction to Natural Language Processing (NLP)

• What is NLP? Key concepts and real-world applications
• The relationship between NLP and machine learning
• Overview of NLP tasks: text classification, sentiment analysis, language modeling

Session 2: Text Preprocessing Techniques

• Text normalization: tokenization, stemming, lemmatization
• Removing stop words, punctuation, and special characters
• Handling noisy text and preparing data for analysis

Session 3: Hands-on Lab: Text Cleaning and Preprocessing

• Working with raw text data in Python
• Applying tokenization, stemming, and lemmatization using NLTK and SpaCy
• Preparing datasets for NLP models

Day 2
Session 1: Feature Extraction in NLP

• Bag of Words (BoW) model and its applications
• Term Frequency-Inverse Document Frequency (TF-IDF) for feature representation
• Introduction to word embeddings (Word2Vec, GloVe)

Session 2: Hands-on Lab: Text Vectorization Techniques

• Implementing BoW and TF-IDF using Scikit-learn
• Visualizing text data for better understanding
• Applying feature extraction techniques to real-world datasets

Session 3: Text Classification with Machine Learning

• Supervised learning for text classification: Naive Bayes, Logistic Regression, Support Vector Machines (SVM)
• Model evaluation metrics: accuracy, precision, recall, F1-score
• Practical applications: spam detection, topic classification

Day 3
Session 1: Hands-on Lab: Building Text Classification Models

• Developing text classification models using Scikit-learn
• Training and testing models with real-world text data
• Optimizing model performance through hyperparameter tuning

Session 2: Sentiment Analysis with Machine Learning

• Understanding sentiment analysis and its business applications
• Rule-based vs. ML-based sentiment analysis techniques
• Case studies: sentiment analysis in social media, customer feedback, and product reviews

Session 3: Hands-on Lab: Sentiment Detection in Real-World Data

• Implementing sentiment analysis models in Python
• Analyzing customer feedback and social media data
• Visualizing sentiment trends and patterns

Day 4
Session 1: Language Modeling and Sequence Processing

• Introduction to language models: n-grams, Markov models
• Overview of advanced models: Recurrent Neural Networks (RNNs), LSTMs, Transformers (conceptual introduction)
• Applications of language modeling: text generation, translation, and summarization

Session 2: Hands-on Lab: Building a Simple Language Model

• Implementing basic language models using Python
• Text generation with n-grams and simple RNNs (conceptual demonstration)
• Evaluating language models for text coherence and relevance

Session 3: Real-World NLP Applications and Capstone Project

• Case studies: chatbots, recommendation systems, and automated text summarization
• Group capstone project: building an end-to-end NLP solution
• Presentation of projects and key takeaways