Python and Gen AI Class Syllabus

Duration : 16 days

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

– Conditional statements (if, elif, else)

– Comparison and logical operators

– Introduction to loops (while loops)

– Using loops for repetitive tasks

For loops can be understood on Day 4 class

Day 4: Data Structures

– Lists: creation, indexing, slicing

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

Day 5: Data Structures (contd..)

– Continue For loop after lists.

– Set Basics

– Set methods

Day 6: Data Structures (contd..)

– Tuples (Didnt add in syllabus) # Just gave an overview

– Dictionaries: creation, accessing values

– Basic dictionary methods

Day 7: Functions and Modules

Day 7: Functions

-Built-in Functions

-Defining and calling functions

– Parameters and return values

Day 8: Modules

– Introduction to modules and libraries

– Using the `math` module

– Introduction to Packages

Day 9: libraries

– Packages continued..

– Understanding PIP

Day 10: Strings, 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

– Review of all Python topics covered

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

Day 11-12,13: Introduction to Generative AI

Day 11,12: 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 13: Code Generation, Prompt Engineering

– Introduction to code generation with AI

– Leveraging Claude / ChatGPT to build tools / softwares

– Introduction to Cursor IDE

– Asking the right questions using better Prompt Engineering

– Practical Exercise : Utilize code generation capabilities and build a simple web page

Day 14-16: Advanced Generative AI Concepts

Day 14: Image Generation / recognition, Running large language models (LLMs) locally (needs GPU)

– Introduction to image generation tools and 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)

– Introduction to open source LLMs

– Ollama Introduction : Setting up Ollama and LM Studio for running the models locally

– Harnessing the speed of Groq and Cerebras – an intro

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

Day 15: 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 qdrant cloud (Code to build the RAG system)

Day 16: Building real AI Projects

– Introduction to Langchain

– What is LlamaIndex and where to use it

– Practical exercise : Build a RAG based question and answering system on a webpage with LlamaIndex

– Open Source world of AI

– AI advanced : Next steps in learning


Get on Udemy