Python and Gen AI course : 15 days live sessions

Python and Gen AI Class Syllabus

Linkedin Profile : https://www.linkedin.com/in/arulbenjamin/

Duration : 15 days (Classes can be extended based on learner’s need)

Day 1-4: Python Fundamentals

Day 1: Introduction to Python

- Introduction to programming and Python

- Overview of Google Colab

- Overview of Github

- Basic syntax: print statements, comments

Day 2: Introduction to Python - Contd..

- Variables and data types (integers, floats, strings)

- Simple input and output using `input()` and `print()`

Day 3: Control Structures

- Comparison and logical operators

- Conditional statements (if, elif, else)

- Introduction to loops (while loops)

- Using loops for repetitive tasks

– For loops can be understood on Day 5 class

Day 4: Data Structures

- Lists: creation, indexing, slicing

- Basic list methods (append, remove, etc.)

– Tuple introduction (W3 schools)

Day 5: Data Structures (contd..)

- Continue For loop after lists.

- Set Basics

- Set methods

- Dictionaries: creation, accessing values

- Basic dictionary methods

Day 6 and 7: Functions, modules and packages

-Built-in Functions

-Defining and calling functions

- Parameters and return values

- Introduction to modules and libraries

- Using the `math` module

- Introduction to Packages

- Understanding PIP

Day 8: Files, Python Project and Review of topics

- String operations and methods

- String formatting

- Reading from and writing to files using Colab's file system

- Basic file operations

- Simple project: Creating a basic python project (Learners have to do based on their understanding)

- Review of all Python topics covered

Day 9-10: Introduction to Generative AI

Day 9: Text Generation Tools and LLMs

- Overview of text generation tools

- LLMs - an Introduction

- Introduction to ChatGPT, Gemini and Claude

- Practical exercise: Comparing ChatGPT vs. Gemini (zero code exercise)

- Using OpenAI Playground and Google AI Studio

- Demo: Google AI Studio, Open AI Playground

Day 10: Code Generation and Prompt Engineering

- Introduction to code generation with AI

- Leveraging Claude / ChatGPT to build tools / softwares

- Asking the right questions using better Prompt Engineering

- Practical exercise: Building a python code generator using gemini AI model and fastAPI (Using AI code generation - zero code exercise) - Cursor IDE

Day 11-14: Advanced Generative AI Concepts

Day 11: Image Generation / recognition

- Introduction to image generation tools

- Use cases for image generation tools

- Overview of tools: OpenAI DALL-E, Midjourney, Stable Diffusion 2

- Practical exercise: Generating image and animate it with runwayML (zero code exercise)

Day 12: Running large language models (LLMs) locally - Needs GPU

- Introduction to open source LLMs

- Ollama Introduction

- Setting up Ollama and LM Studio for running the models locally

- Accessing local models with code

- Practical exercise: Creating a simple chatbot as like ChatGPT with Ollama and LMStudio (zero code exercise)

Day 13: Retrieval Augmented Generation

- RAG technique to use LLMs with our own Data

- Embeddings and vector stores (chromaDB, qdrant, pgvector)

- leveraging RAG to use our own data without exposing it to Model

- Practical exercise : python code exercise to build a RAG pipeline for chunking and storing the PDF in FAISS (Code to build the RAG system)

Day 14: Langchain and LlamaIndex (LLM Frameworks)

- Introduction to Langchain

- What is LlamaIndex and where to use it

- Practical exercise :

1. Build a question and answering system on a webpage with LlamaIndex and ChromaDB

Day 15: Building real AI Projects

- Chat with SQL using Langchain - My github project demo

- Open Source world of AI

- AI advanced : Next steps in learning