Python for Beginners + Generative AI & Claude Practice Test

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