Program Structure

Module 1: Introduction to NLP and Text Preprocessing

• Overview of NLP: Key concepts, applications, and challenges.
• Text preprocessing techniques: Tokenization, stopword removal, lemmatization, and stemming.
• Building your first NLP pipeline: Text cleaning and preparing data for analysis.

Module 2: Exploring NLP with Python

• Introduction to popular Python libraries for NLP: NLTK, SpaCy, and Gensim.
• Hands-on exercises: Text analysis, frequency distribution, and word tokenization.
• Working with word embeddings: Word2Vec, GloVe, and FastText.

Module 3: Text Classification and Sentiment Analysis

• Building text classification models for tasks like spam detection and sentiment analysis.
• Techniques for text feature extraction: Bag-of-Words, TF-IDF, and Word Embeddings.
• Hands-on project: Sentiment analysis with real-world datasets (e.g., movie reviews, social media posts).

Module 4: Named Entity Recognition (NER) and Part-of-Speech Tagging

• Understanding named entity recognition (NER) and its applications in text analysis.
• Building models for entity extraction: Dates, locations, organizations, and more.
• Part-of-speech tagging and syntactic parsing to understand sentence structures.

Module 5: Advanced NLP Models: Transformers and BERT

• Introduction to Transformer architecture and its significance in NLP.
• Understanding BERT (Bidirectional Encoder Representations from Transformers) and its applications in NLP.
• Implementing pre-trained transformer models for text classification and question answering.

Module 6: Text Generation and Chatbot Development

• Building NLP-powered chatbots and conversational agents.
• Text generation techniques using Recurrent Neural Networks (RNNs) and Transformers.
• Exploring GPT models for language generation: Text completion, translation, and summarization.

Module 7: NLP Applications in Real-World Scenarios

• Applications of NLP in various industries: Healthcare, finance, customer service, and marketing.
• Using NLP for information retrieval and building recommendation systems.
• Hands-on project: Applying NLP to a real-world domain like analyzing customer reviews or building a recommendation engine.

Module 8: Model Evaluation and Deployment in NLP

• Evaluating the performance of NLP models using metrics like precision, recall, F1-score, and confusion matrix.
• Fine-tuning and optimizing NLP models for better performance.
• Deploying NLP models to production environments: API development and cloud deployment.

Module 9: Ethical Considerations and Bias in NLP

• Understanding ethical challenges in NLP: Bias, fairness, and inclusivity in language models.
• Approaches to mitigate bias in NLP algorithms and datasets.
• Building responsible AI models for NLP that align with ethical guidelines.

Final Project

• Develop a comprehensive NLP solution to solve a real-world problem in any domain (e.g., healthcare, finance, social media).
• Implement a text classification, sentiment analysis, or chatbot system based on your project requirements.
• Example projects: Sentiment analysis for social media monitoring, chatbots for customer service, or a document classification system for legal documents.