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Generative AI Bootcamp with Python: Gen AI & Agentic AI
Rating: 4.6 out of 5(56 ratings)
507 students

Generative AI Bootcamp with Python: Gen AI & Agentic AI

Learn Generative AI, build Agentic AI systems, and master prompt engineering with hands-on Python projects and apps
Created byZahid Halim
Last updated 2/2026
English

What you'll learn

  • Master Generative AI from scratch – Learn GANs, VAEs, Transformers & Diffusion Models, even as a beginner
  • Hands-on AI projects – Build text, image & music generation projects to showcase real-world skills
  • Write industry-ready Python code – Use TensorFlow/Keras, Git, and Docker for clean, reproducible AI projects
  • Boost career & research opportunities – Learn cutting-edge generative AI to stand out in ML, data science & research

Course content

4 sections • 26 lectures • 7h 17m total length

  • Introduction1:42

    Explore generative ai with Python from basics to advanced models through hands-on coding. Learn concepts and implement gan, transformers, diffusion models, autoencoders, and normalizing flows, building projects for your resume.

  • GenAI-AI Introduction8:15

    Explore the core concepts of artificial intelligence, from data and algorithms to models and learning, and see how generative AI creates new content.

  • Generative modeling6:10
  • Quiz-1: Understand AI basics and probability foundations,
  • Our First Generative Model6:56
  • Representative learning5:56
  • Quiz-2
  • Core Probability Theory4:31

    Explore core probability concepts for generative modeling, including the sample space, density function, and likelihood, and apply maximum likelihood estimation to identify parameters that explain observed data.

  • Basics of the Coding Environment4:17

    Explore basics of the coding environment for generative AI with Python: clone GitHub repo, install Git, Docker, and run notebooks on CPU or GPU with Keras, TensorFlow, PyTorch, Jax.

  • Quiz-3
  • Git clone & Dockers6:10
  • Quiz-4
  • Setting up the IDE10:06
  • Quiz-5

Requirements

  • Basic Python knowledge, high school-level math, a computer with internet, and enthusiasm to learn AI—no prior AI experience needed.

Description

Step into the future of technology with our hands-on AI and Generative Deep Learning course! From understanding the foundations of AI and probability theory to building advanced neural networks and generative models like GANs, VAEs, and Diffusion Models, this course equips you with the skills to create cutting-edge AI applications.

Learn by doing: set up your environment with Git, Docker, and IDEs, implement ANNs, CNNs, LSTMs, and master representation learning. Dive into generative architectures and see your ideas come alive through music generation, advanced GAN projects, and transformer-based applications.

Whether you’re an aspiring AI engineer, researcher, or tech enthusiast, this course turns complex concepts into hands-on projects, making you industry-ready. Unlock your potential, create AI-driven solutions, and be part of the next generation of AI innovators!

Gain deep insights into probability theory, coding environments, and the latest AI techniques. Explore real-world applications, improve your programming skills, understand model deployment, and learn best practices for optimizing model performance. By the end, you will confidently design, train, and evaluate generative models, turning your ideas into tangible, innovative projects that can impress both academia and industry.

Why Enroll?

  • Hands-on projects from setup to deployment

  • Learn cutting-edge generative AI models

  • Step-by-step guidance for real-world applications

  • Perfect for beginners and advanced learners alike

  • Enhance your portfolio with unique, creative AI projects

Who this course is for:

  • This course is designed for beginners and intermediate learners who want to master Generative AI with Python
  • Ideal for: Students, professionals, or hobbyists interested in AI and machine learning.
  • Developers and data scientists aiming to build real-world AI projects.
  • Anyone wanting hands-on experience with GANs, VAEs, Transformers, and Diffusion Models.
  • Learners seeking a career boost or research opportunities in AI, data science, or deep learning.

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