Natural Language Processing (NLP) Class Outline

Overview

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) focused on enabling machines to understand, interpret, and respond to human language. This course introduces foundational concepts, state-of-the-art techniques, and applications of NLP, combining theoretical understanding with hands-on practical experience. By the end of the course, students will have a comprehensive understanding of how language-driven AI systems are built and deployed.

Objectives

1. Understand the core principles and challenges of Natural Language Processing.

2. Learn text preprocessing and feature extraction techniques.

3. Explore classical and statistical approaches to text analysis.

4. Gain proficiency in using machine learning and deep learning for NLP tasks.

5. Develop practical skills using popular NLP frameworks and tools.

6. Build and deploy NLP applications for real-world use cases such as chatbots, sentiment analysis, and text summarization.

7. Discuss ethical considerations in language technology, including bias and privacy.

Length: 4 Days

1. Introduction to Natural Language Processing

• What is NLP?

  • Definition and significance

  • Key challenges in NLP

• Applications of NLP

  • Examples: Chatbots, Sentiment Analysis, Machine Translation

• History of NLP

  • Milestones in NLP development

• NLP Pipeline

  • Steps: Tokenization, Parsing, Feature Extraction, Modeling

2. Fundamentals of Linguistics for NLP

• Language Basics

  • Syntax, Semantics, Pragmatics

• Text Preprocessing

  • Tokenization

  • Lemmatization and Stemming

  • Stopword Removal

  • Text Normalization (e.g., lowercasing, removing special characters)

3. Statistical and Classical Approaches to NLP

• N-gram Models

  • Definition and usage

  • Limitations of N-gram models

• Text Similarity

  • Cosine Similarity

  • Jaccard Similarity

• Topic Modeling

  • Latent Dirichlet Allocation (LDA)

  • Non-Negative Matrix Factorization (NMF)

• TF-IDF (Term Frequency-Inverse Document Frequency)

4. Machine Learning for NLP

• Supervised Learning in NLP

  • Text Classification

  • Named Entity Recognition (NER)

• Unsupervised Learning in NLP

  • Clustering

  • Topic Modeling

• Feature Engineering

  • Bag of Words (BoW)

  • Word Embeddings (Word2Vec, GloVe)

• Evaluation Metrics

  • Precision, Recall, F1-Score

  • BLEU, ROUGE for text generation

5. Deep Learning for NLP

• Introduction to Deep Learning

  • Neural Networks Overview

  • Key differences from traditional ML in NLP

• Recurrent Neural Networks (RNNs)

  • LSTMs and GRUs

• Attention Mechanisms

  • Self-Attention and Transformers

• Transfer Learning in NLP

  • Pretrained Models: BERT, GPT, RoBERTa

• Fine-Tuning and Domain Adaptation

6. Advanced Topics in NLP

• Natural Language Generation (NLG)

  • Text Summarization

  • Machine Translation

• Question Answering Systems

  • Building QA models

• Sentiment Analysis

  • Real-world applications

• Speech-to-Text and Text-to-Speech

• Ethics in NLP

  • Bias in language models

  • Privacy concerns

7. Tools and Frameworks for NLP

• Python Libraries

  • NLTK

  • spaCy

  • Gensim

• Deep Learning Frameworks

  • TensorFlow

  • PyTorch

  • Hugging Face Transformers

• Data Sources

  • Common datasets: IMDB, Wikipedia, Kaggle datasets

8. NLP in Practice

• Project Work

  • Building Chatbots

  • Sentiment Analysis on Social Media Data

  • Fake News Detection

• Deployment

  • APIs (Flask/Django)

  • Deployment on Cloud (AWS, GCP, Azure)

• Performance Optimization

  • Fine-tuning models

  • Handling large datasets

9. Capstone Project

• Collaborative projects where students build end-to-end NLP systems.