This 16-lecture course is designed to provide a solid foundation in Python programming and an introduction to Generative AI. Tailored for beginners, the course includes both theoretical lessons and hands-on projects to ensure that learners can apply their knowledge in real-world scenarios. The entire course is more of a story telling format to beginners in realtime. The recordings can give you an immersive experience in class.

Lecture 1: Introduction to Generative AI and Python

• Overview of the course structure and objectives.
• Introduction to Python and its importance in AI.
• Overview of Generative AI, including its applications and relevance in today’s world.

Python Fundamentals (Lectures 2–10)

Lecture 2: Introduction to Python Basics

• Overview of programming and Python as a language.
• Setting up and using Google Colab for coding.
• Exploring GitHub for code storage and collaboration.
• Basic syntax in Python: print statements, comments.

Lecture 3: Variables and Data Types

• Understanding variables and their role in programming.
• Exploring different data types: integers, floats, strings.
• Simple input and output operations using input() and print() functions.

Lecture 4: Control Structures

• Conditional statements: if, elif, else.
• Comparison and logical operators.
• Introduction to loops: while loops and their use in repetitive tasks.

Lecture 5: Lists and For Loops

• Lists: creation, indexing, slicing, and basic list methods.
• Introduction to for loops and their applications in iterating through lists.

Lecture 6: Sets and Loops

• Working with sets: creation and methods.
• Continuation of for loops, applied to sets and other data structures.

Lecture 7: Tuples and Dictionaries

• Overview of tuples: creation and properties.
• Working with dictionaries: creation, accessing values, and basic dictionary methods.

Lecture 8: Functions in Python

• Understanding and using built-in functions.
• Defining custom functions, parameters, and return values.

Lecture 9: Modules and Libraries

• Introduction to Python modules and libraries.
• Using the math module and understanding Python packages.
• Introduction to PIP for managing Python libraries.

Lecture 10: String Operations and File Handling

• String operations and formatting.
• Reading from and writing to files using Google Colab’s file system.
• Hands-on project: Create a simple Python project to demonstrate understanding of Python fundamentals.

Introduction to Generative AI (Lectures 11–13)

Lecture 11-12: Text Generation and LLMs

• Overview of text generation tools and Large Language Models (LLMs) like ChatGPT, Gemini, and Claude.
• Hands-on exercises using OpenAI Playground and Google AI Studio for text generation.
• Practical comparison of outputs from different AI tools.

Lecture 13: AI-driven Code Generation and Prompt Engineering

• Introduction to AI-based code generation using tools like ChatGPT and Claude.
• Understanding Cursor IDE for AI-assisted coding.
• Practical project: Build a simple web page using AI-generated code.

Advanced Generative AI Concepts (Lectures 14–16)

Lecture 14: Image Generation and Running LLMs Locally

• Overview of image generation tools such as DALL-E, Midjourney, and Stable Diffusion.
• Practical exercise: Generating and animating images using runwayML.
• Running open-source LLMs locally using tools like Ollama and LMStudio.

Lecture 15: Retrieval Augmented Generation (RAG)

• Using LLMs with custom data through RAG techniques.
• Introduction to embeddings and vector stores (chromaDB, qdrant).
• Practical exercise: Building a RAG pipeline to process and store PDFs in qdrant cloud.

Lecture 16: Building Real AI Projects

• Introduction to Langchain and LlamaIndex.
• Hands-on project: Create a RAG-based question-answering system on a webpage.
• Exploring the open-source AI ecosystem and next steps for continued learning.

By the end of the course, learners will have gained a thorough understanding of Python programming and practical experience with Generative AI, enabling them to build AI-driven projects.