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
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Overview of the course structure and objectives.
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Introduction to Python and its importance in AI.
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Overview of Generative AI, including its applications and relevance in today’s world.
Python Fundamentals (Lectures 2–10)
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Lecture 2: Introduction to Python Basics
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Overview of programming and Python as a language.
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Setting up and using Google Colab for coding.
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Exploring GitHub for code storage and collaboration.
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Basic syntax in Python: print statements, comments.
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Lecture 3: Variables and Data Types
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Understanding variables and their role in programming.
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Exploring different data types: integers, floats, strings.
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Simple input and output operations using input() and print() functions.
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Lecture 4: Control Structures
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Conditional statements: if, elif, else.
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Comparison and logical operators.
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Introduction to loops: while loops and their use in repetitive tasks.
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Lecture 5: Lists and For Loops
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Lists: creation, indexing, slicing, and basic list methods.
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Introduction to for loops and their applications in iterating through lists.
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Lecture 6: Sets and Loops
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Working with sets: creation and methods.
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Continuation of for loops, applied to sets and other data structures.
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Lecture 7: Tuples and Dictionaries
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Overview of tuples: creation and properties.
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Working with dictionaries: creation, accessing values, and basic dictionary methods.
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Lecture 8: Functions in Python
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Understanding and using built-in functions.
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Defining custom functions, parameters, and return values.
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Lecture 9: Modules and Libraries
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Introduction to Python modules and libraries.
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Using the math module and understanding Python packages.
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Introduction to PIP for managing Python libraries.
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Lecture 10: String Operations and File Handling
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String operations and formatting.
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Reading from and writing to files using Google Colab’s file system.
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Hands-on project: Create a simple Python project to demonstrate understanding of Python fundamentals.
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Introduction to Generative AI (Lectures 11–13)
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Lecture 11-12: Text Generation and LLMs
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Overview of text generation tools and Large Language Models (LLMs) like ChatGPT, Gemini, and Claude.
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Hands-on exercises using OpenAI Playground and Google AI Studio for text generation.
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Practical comparison of outputs from different AI tools.
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Lecture 13: AI-driven Code Generation and Prompt Engineering
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Introduction to AI-based code generation using tools like ChatGPT and Claude.
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Understanding Cursor IDE for AI-assisted coding.
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Practical project: Build a simple web page using AI-generated code.
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Advanced Generative AI Concepts (Lectures 14–16)
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Lecture 14: Image Generation and Running LLMs Locally
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Overview of image generation tools such as DALL-E, Midjourney, and Stable Diffusion.
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Practical exercise: Generating and animating images using runwayML.
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Running open-source LLMs locally using tools like Ollama and LMStudio.
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Lecture 15: Retrieval Augmented Generation (RAG)
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Using LLMs with custom data through RAG techniques.
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Introduction to embeddings and vector stores (chromaDB, qdrant).
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Practical exercise: Building a RAG pipeline to process and store PDFs in qdrant cloud.
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Lecture 16: Building Real AI Projects
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Introduction to Langchain and LlamaIndex.
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Hands-on project: Create a RAG-based question-answering system on a webpage.
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Exploring the open-source AI ecosystem and next steps for continued learning.
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Course Features:
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Hands-on Practice: Each lecture includes Python coding exercises, quizzes, and practical projects.
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Practice Questions: Focused on real-world scenarios to help reinforce concepts.
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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.
