Jumpstart Python & Gen AI: Zero to Hero for Beginners

Here’s the updated course description including practice questions and a Python coding exercise:

Course Description:

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 follows a storytelling format for beginners, offering an immersive experience through recorded class sessions.

Course Structure:

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.

Course Features:

• Hands-on Practice: Each lecture includes Python coding exercises, quizzes, and practical projects.
• Practice Questions: Focused on real-world scenarios to help reinforce concepts.
• Python Coding Exercise: Aimed at applying Python fundamentals to build meaningful applications.

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.