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
