Generative AI, LLMs & Agentic AI for Modern Applications

Generative AI, LLMs & Agentic AI for Modern Applications
(In Association with iHUB Divyasampark IIT Roorkee)

About the Course:

Course Objectives:

• To introduce participants to core AI concepts, including Natural vs Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI fundamentals.

• To enable learners to do Data Processing for automation, data handling, and API integration (including OpenAI API).

• To introduce participants to Natural Language Processing (NLP) and equip them to build text-processing pipelines including tokenization, sentiment analysis, and custom text classifiers.

• To make learners proficient in working with Large Language Models (LLMs), covering transformer architecture, GPT, BERT, Hugging Face, and LangChain for chatbot development and text generation.

• To equip learners with the knowledge of LangChain framework, LangGraph, and Retrieval Augmented Generation (RAG) for building advanced conversational agents and memory-enabled systems.

• To familiarize learners with Vector Databases (e.g., Pinecone) and their applications in semantic search, recommendation engines, and biomedical research.

• To introduce learners to Speech Recognition and Speech-to-Text systems using traditional ML, Deep Learning, and transformer-based approaches such as Whisper AI.

• To prepare learners for LLM Engineering, including prompt engineering, hosting models vs APIs, cost optimization, scaling strategies, and deploying AI-powered applications with Streamlit.

Prerequisites and eligibility:

Course Overview:

   Module 1: Foundations of Artificial Intelligence & Generative AI

• Building an AI Tool: Overview of the AI application development workflow.
• Artificial Intelligence: Natural vs Artificial Intelligence, brief history and Weak vs Strong AI.
• AI Ecosystem: AI vs Data Science vs Machine Learning vs Deep Learning 
• Data Basics: Data collection, labelled vs unlabelled, structured vs unstructured; metadata
• Machine & Deep Learning: Quick overview of Important Aspects & Applications.
• Natural Language Processing (NLP): Introduction to NLP and its applications 
• Generative AI: Concepts, evolution, applications and introduction to ChatGPT.
• Large Language Models (LLMs): Evolution from N-Grams and RNNs to Transformers; training and inference concepts
• Generative AI Development: Prompt Engineering, Fine-Tuning & RAG-conceptual overview 
• Foundation Models: Open-source vs proprietary models and model selection
• Challenges of Generative AI: Hallucination, inconsistency, bias, data limitations, latency, cost and privacy/security.
• AI Stack: Python, LLM APIs, Hugging Face, LangChain &Vector Databases-an Overview.·

   Module 2:  Data Processing for Generative AI

• NumPy Library: Arrays, indexing, slicing, reshaping and vectorized operations.
• Pandas: Series, DataFrames, data types and basic data manipulation. 
• Data Manipulation: Selection, filtering, sorting, indexing (loc/iloc) and groupby operations.
• Data Cleaning & Preprocessing: Missing values, duplicates, data type conversion and basic preprocessing.
• Text Data Processing: String operations, text cleaning and Regular Expressions (Regex).
• File & Data Handling: Reading and writing CSV, JSON and text files.
• Data Exploration: Loading, inspecting, transforming and exporting datasets.

   Module 3: Natural Language Processing (NLP) & Text Analytics

• NLP Fundamentals: Introduction, applications & NLP workflow; supervised vs. unsupervised.
• Text Preprocessing: Text cleaning, normalization, stop-word removal and Regular Expressions.
• Text Representation: Tokenization, stemming, lemmatization and N-grams
• Linguistic Analysis: POS tagging and Named Entity Recognition (NER)
• Text Classification: Sentiment analysis and traditional ML-based text classification
• Numerical Representation: Bag-of-Words, TF-IDF and text feature extraction
• Modern NLP: Introduction to embeddings and pre-trained Transformer-based NLP models
• Hands-on: Build a Text Classification & Sentiment Analysis System using Python and NLP

Module 4:  File Handling, Data Processing & Automation

• File I/O & File Operations: Reading, writing and appending files; file modes, line-by-line file processing, file pointers, read()/write() operations and context managers using with statement.
• File & Directory Management: Listing, checking, copying, moving and deleting files and directories using os and shutil
• File & Directory Automation: Automating file and directory management tasks, including organizing files by type and working with nested directories
• Command-Line Utilities: Creating command-line tools using Python's argparse module; command-line arguments, argument types, help messages, choices and argument parsing
• Command-Line Automation: Building Python scripts that accept user inputs and perform operations directly from the terminal
• Error Handling in File Operations: Handling missing files, invalid paths and file-related errors using exceptions and try-except
• Hands-on Project: Automated File Management / Command-Line Utility

Module 5:  Python Libraries, Packages, APIs & Virtual Environment

• Python Libraries & Modules: Python Standard Library, built-in modules, importing modules and packages, external/third-party libraries, package documentation and module usage.
• Package Management: Installing, upgrading, uninstalling and managing third-party packages using pip; checking installed packages with pip list; installing specific package versions.
• Virtual Environments: Creating, activating and deactivating isolated Python environments; managing project-specific dependencies and package versions.
• Dependency Management: Generating and using requirements.txt; freezing package versions with pip freeze; installing project dependencies with pip install -r
• APIs & HTTP Requests: Using the requests library to make GET and POST requests, retrieving data from URLs, sending request data and working with different HTTP request methods
• API Data Processing: Processing text and JSON responses from external APIs and saving API responses to files
• Regular Expressions: Pattern matching and text processing using Python's re module; searching, finding multiple matches, case-insensitive matching and replacing patterns
• Multithreading & Parallel Execution: Thread-based parallel execution for I/O-bound tasks; creating and starting threads, passing arguments, using join() and understanding the Global Interpreter Lock (GIL)
• Multiprocessing: Introduction to multiprocessing for CPU-bound tasks and understanding when multiprocessing is preferred over multithreading
• Hands-on Project: Developing a Reusable API Integration Tool with Dependency Management, Regular Expression Processing and Parallel Execution

Module 6: Version Control: Git for Developers

• Git Fundamentals: Version control concepts, Git workflow, repositories, installation and configuration
• Repository Management: Creating and cloning repositories, .gitignore, repository structure and remote repositories
• Tracking & Managing Changes: Working directory, staging area, commits, git status, git add, git diff and commit history
• Branching & Merging: Creating and managing branches, merging, resolving merge conflicts and basic branching strategies
• Remote Collaboration: GitHub/GitLab, push, pull, fetch, pull requests and collaborative development
• Undoing & Recovering Changes: restore, reset, revert, stash, tags and version recovery
• Hands-on: Managing and Collaborating on a Python Project using Git & GitHub

Module 7: AI-assisted Python Development & Capstone Project

• AI-Assisted Python Development: When and how to use AI tools effectively for Python programming, debugging, code explanation and problem-solving
• AI Coding Assistants: Practical use of ChatGPT, GitHub Copilot, Cursor AI and other AI-powered coding assistants for Python development
• Working with LLM APIs: Introduction to LLM APIs, API requests, authentication, prompts, response handling and integrating LLM capabilities into Python applications
• Capstone Project: Build an AI-powered virtual assistant using Python and OpenAI. Create a chatbot with an API key, chat completions, and web or mobile app integrations.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile: Mr. Shreyas Shukla

       


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