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.
