Day 1
Session 1: Introduction to Text Classification and NER

• What is text classification? Key concepts and business applications
• Introduction to Named Entity Recognition (NER) and its importance in data extraction
• Overview of NLP pipelines and workflows

Session 2: Text Preprocessing for NLP

• Cleaning and normalizing text data (removing stop words, punctuation, and special characters)
• Tokenization, stemming, and lemmatization techniques
• Introduction to Python libraries for NLP: NLTK, spaCy, and Scikit-learn

Session 3: Hands-on Lab: Text Preprocessing with Python

• Working with real-world datasets (e.g., news articles, customer feedback)
• Implementing tokenization, stemming, and lemmatization using NLTK and spaCy
• Preparing datasets for text classification and NER models

Day 2
Session 1: Building Text Classification Models

• Feature extraction techniques: Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF)
• Introduction to machine learning algorithms for text classification: Naive Bayes, Logistic Regression, and Support Vector Machines (SVM)
• Model evaluation metrics: accuracy, precision, recall, F1-score

Session 2: Hands-on Lab: Implementing Text Classification Models

• Feature extraction using BoW and TF-IDF
• Building text classification models using Scikit-learn
• Evaluating model performance with real-world datasets

Session 3: Introduction to Topic Modeling

• What is topic modeling? Key concepts and applications
• Overview of Latent Dirichlet Allocation (LDA) for topic discovery
• Visualizing topic models to identify hidden themes in text data

Session 4: Hands-on Lab: Topic Modeling with LDA

• Applying LDA to real-world datasets (e.g., news articles, product reviews)
• Interpreting topic modeling results for business insights
• Optimizing topic models for better performance

Day 3
Session 1: Named Entity Recognition (NER) with NLP

• Understanding NER: entity types (persons, organizations, locations, dates)
• Rule-based vs. machine learning-based NER approaches
• Real-world applications of NER: information extraction, compliance monitoring, and knowledge graphs

Session 2: Hands-on Lab: Implementing NER with spaCy

• Building NER models using pre-trained pipelines in spaCy
• Customizing NER models for domain-specific applications
• Evaluating NER performance and fine-tuning models

Session 3: Real-World Case Studies and Business Applications

• Case study: Text classification for automated document categorization
• Case study: NER for extracting key entities from legal and financial documents
• Group activity: Designing an NLP solution for a business problem

Session 4: Challenges and Best Practices in NLP

• Common challenges in text classification and NER (e.g., ambiguity, data imbalance)
• Best practices for model deployment and maintenance
• Ethical considerations in NLP applications (bias, privacy, data security)