Udemy - Full Stack Generative AI: Deep Learning, CNN, LLM Agentic AI Free Download

Udemy – Full Stack Generative AI: Deep Learning, CNN, LLM Agentic AI
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Python, NumPy, Pandas, Matplotlib, Deep Learning, Generative AI : GAN, VAE, LLMs, RAG, MCP, ACP, A2A, Agentic AI & more

This comprehensive course is your one-stop guide to learn Python Basics, Popular Data Manipulation Libraries, Deep Learning Fundamentals, Popular Generative AI Models, Large Language Models and Agentic AI frameworks, all in one place. Whether you’re a beginner exploring the world of AI or a developer looking to level up, this course takes you from the ground up and beyond.

We begin with Python fundamentals and dive into essential data libraries like NumPy, Pandas, and Matplotlib for effective data handling and visualization. Then, we advance into Deep Learning, building and training neural networksMode to understand the core mechanics behind AI.

Generative AI is a subset of Deep Learning. Without a solid understanding of Deep Learning fundamentals, learning Generative AI becomes difficult and often confusing. That’s why I’ve combined the most essential parts from one of my previous Deep Learning courses into this course. This ensures that you build a strong foundation before diving into advanced Generative AI topics.

Once the Deep Learning Fundamentals is complete, You’ll then explore the rapidly evolving field of Generative AI:From training your own GANs and VAEs, to working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Diffusion Models, this course offers hands-on projects and intuitive explanations.

Finally, we introduce you to the next frontier: Agentic AI. Learn about intelligent agent architectures such as MCP, ACP, and A2A, and use cutting-edge frameworks like LangChain to build autonomous, goal-driven AI agents.

What You’ll Learn

Python programming basics and data manipulation using NumPy and Pandas

Data visualization using Matplotlib

Fundamentals of Deep Learning and neural network training

Building Generative AI models: GANs, VAEs, LLMs, and Diffusion Models

Implementing Retrieval-Augmented Generation (RAG)

Understanding and applying Agentic AI Protocols: MCP, ACP, A2A

Working with popular Agentic AI frameworks like LangChain

Requirements

No prior programming or AI experience is required

A basic understanding of high-school math is helpful

Access to a computer with internet connection

Curiosity and a willingness to learn by building real-world projects

The code, and jupyter notebook files used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.

Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.

So that’s all for now, see you soon in the class room. Happy learning and have a great time.

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