In today’s data-driven world, understanding human language is a superpower. This course explores Natural Language Processing (NLP) and Large Language Models (LLMs) — the cutting-edge technologies behind intelligent chatbots, search engines, and AI systems that understand and generate human text.

You will learn how machines actually “read” and analyze text, from basic preprocessing all the way to working with state-of-the-art pre-trained models. You will apply NLP to real-world problems like sentiment analysis, topic discovery, named entity recognition, semantic search, and text classification. By the end of this course, you will be equipped to build practical NLP applications and understand how modern LLMs work.

Python Ecosystem and Tools

• NLP Libraries: NLTK, spaCy, TextBlob
• Text Processing: scikit-learn (TfidfVectorizer, CountVectorizer)
• Word Embeddings: gensim (Word2Vec, Doc2Vec), GloVe, FastText
• Topic Modeling: gensim (Latent Dirichlet Allocation – LDA)
• Pre-trained Models: Hugging Face Transformers, BERT, GPT models
• LLMs & RAG: LangChain, OpenAI API, Hugging Face Hub
• Vector Databases: Pinecone, Weaviate, ChromaDB, Faiss
• Visualization: Matplotlib, Seaborn, Plotly, spaCy visualizers
• Evaluation & Metrics: scikit-learn (precision, recall, F-1), BLEU, ROUGE
• Computing: Jupyter Notebook, Google Colab

Disclaimer: The tools, technologies, and learning outcomes listed in this syllabus are subject to change based on industry developments, software availability, or course improvements. Students will be notified in advance of any adjustments.

Learning Outcomes

By the end of this course, students and professionals will be able to:

1. Grasping NLP Fundamentals & Real-World Applications
   • Understand what NLP is, why it matters, and how it is applied in industry — from search engines to customer service chatbots.
   • Recognize common NLP tasks and know which techniques work best for different problems.
2. Preparing Text Data for Analysis
   • Explore and analyze textual datasets, uncovering patterns and characteristics that guide your modeling strategy.
   • Clean, tokenize, and preprocess raw text using industry-standard libraries and techniques.
3. Representing Text as Numbers
   • Transform text into numerical representations using bag-of-words, n-grams, TF-IDF, and word embeddings.
   • Understand why word embeddings like Word2Vec and GloVe capture semantic meaning better than older methods.
4. Topic Modeling & Unsupervised Learning
   • Discover hidden topics within large collections of text using Latent Dirichlet Allocation (LDA).
   • Extract themes and insights from unstructured documents without labeled data.
5. Leveraging Pre-trained Models & Transfer Learning
   • Use powerful pre-trained language models (like BERT) instead of building from scratch, saving time and computational resources.
   • Fine-tune and adapt existing models for your specific NLP task or domain.
6. Building NLP Solutions with LLMs & RAG
   • Understand the basics of Large Language Models and how they generate human-like text.
   • Build Retrieval-Augmented Generation (RAG) systems using LangChain that combine LLMs with external knowledge.
7. Evaluating Models & Solving Real Problems
   • Evaluate NLP model performance using appropriate metrics for different tasks (classification, retrieval, generation).
   • Apply NLP techniques to real-world problems: sentiment analysis, named entity recognition, text classification, and semantic search.
8. Semantic Search & Vector Databases
   • Understand vector databases and how semantic search enables finding similar documents by meaning, not just keywords.
   • Build and deploy semantic search systems that understand the intent behind user queries.
9. Building Intelligent AI Agents
   • Understand the concept of AI agents — autonomous systems that can reason, plan, and take action to accomplish goals.
   • Build agents that combine LLMs, tool use, and memory to solve complex, multi-step problems without human intervention.
   • Design agent workflows using frameworks like LangChain that enable decision-making, tool integration, and iterative reasoning.