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Module 1

Installing Python, VS Code, Jupyter Notebook

Setup Fundamentals

Install Python, VS Code, Jupyter; virtual environments (venv/conda); Hello World program; variables, data types, and operators.

Module 2

🔀

Control Flow Statements

Conditionals Loops

Conditionals (if/else/match), loops (for, while), and string manipulation for program control and iteration.

Module 3

⚙️

Functions in Python

Functions Recursion

Defining and calling functions, arguments, return values, default and keyword arguments, lambda functions, and recursion techniques.

Module 4

📦

Data Structures

Collections Comprehensions

Lists, tuples, sets, dictionaries with indexing, slicing, operations, comprehensions; practice with student grades manager and word frequency counter.

Module 5

📂

File Handling

I/O Operations Formats

Reading and writing text files, working with CSV and JSON files, with open() usage, and logging basics for applications.

Module 6

⚠️

Exceptions

Error Handling Robustness

Try/except/finally for error handling, else cases in exceptions, and raising exceptions for robust, fault-tolerant code.

Module 7

📡

Python Libraries & APIs

Package Management REST APIs

Installing packages with pip, using requests library, REST API basics, calling public APIs (weather, crypto), parsing JSON, and API error handling.

Module 8

🧠

Foundation of LLM

Large Language Models Training Concepts

What is a Large Language Model, how LLMs are trained (tokens, datasets, parameters), capabilities (generation, summarization, translation, reasoning), limitations (bias, hallucinations, context length).

Module 9

✨

Generative AI Concepts

AI Evolution Transformers

Difference between traditional AI and Generative AI, transformer architecture and attention mechanisms, common LLM tasks, token understanding, LLM strengths and weaknesses.

Module 10

🔑

Getting Started with OpenAI API

OpenAI Setup Authentication

Setting up OpenAI account, generating and securing API keys with .env file, installing openai Python library, and making first API calls.

Module 11

💬

Prompt Engineering

Techniques Best Practices

What is prompt engineering, rules of effective prompting, zero-shot and few-shot prompting, priming, context setting, tone, persona, avoiding hallucinations, formatting outputs, common pitfalls.

Module 12

🎯

Advanced Prompts with Python

Dynamic Generation Automation

Using variables in prompts, f-strings and .format() methods, batch prompt generation, and building flexible, reusable prompt systems with Python.

Module 13

📋

Prompt Templates

Reusability Modularity

Reusable templates with Python dictionaries, modularity in prompt design, and prompt chaining basics for complex workflows and multi-step processes.

Module 14

🔗

Chain-of-Thought Prompting

Reasoning Steps Accuracy Improvement

Breaking down reasoning into steps, chain-of-thought prompting vs direct prompts, improving model accuracy through step-by-step logic and intermediate reasoning.

Module 15

📚

Retrieval-Augmented Generation (RAG)

Context Extension Vector Databases

Why RAG matters, overcoming model memory limits, vector databases (ChromaDB, FAISS), embeddings, document search, and building knowledge bases for AI systems.

Module 16

📝

Working with JSON Outputs

Structured Data Parsing & Validation

Getting structured responses from LLMs, parsing and validating JSON outputs, and handling errors with malformed or unexpected responses.

Module 17

🤖

Capstone Project: Building a Chatbot

Project Deployment

Chatbot architecture overview, single-turn chatbot with GPT, adding session-based memory, conversation history management, and deploying a functional chatbot application.

Gen AI Path