This training for Generative Adversarial Networks (GANs) from kodestree is designed for beginners who want to start their journey in Generative AI. The course explains GAN concepts in a simple and easy-to-follow manner, covering generators, discriminators, image generation, and real-world applications. Through practical demonstrations and hands-on activities, you will build a strong foundation in GANs and understand how this technology is used to create AI-generated content.
Prerequisites
This course is built specifically for beginners, so the bar is intentionally low. Here’s all you need before starting:
- Basic familiarity with Python – you should know what a loop and a function look like
- A computer with internet access (all training runs in Google Colab – no expensive hardware needed)
- Curiosity about AI and a willingness to learn something new
- No prior machine learning or deep learning experience required
- No advanced mathematics required – we explain what you need, when you need it
Course Objectives
- Understand what GANs are and why they matter in today’s AI world
- Learn how the Generator and Discriminator networks work together
- Get comfortable with key deep learning terms – without the jargon overload
- Write and run your first GAN model using Python and PyTorch
- Understand what training a GAN looks like and what can go wrong
- Generate your own images using a trained GAN
- Build confidence to explore more advanced AI topics after this course
What You Will Learn
- What generative AI is and how GANs fit into the bigger AI picture
- The simple game-theory concept that makes GANs tick
- How neural networks are structured and trained – explained from zero
- The roles of the Generator and Discriminator in plain English
- How to set up your Python coding environment for AI projects
- Building a simple Vanilla GAN step by step with guided code
- What loss functions do in a GAN and why they matter
- How to read training output and tell if your GAN is actually learning
- What common beginner mistakes look like – and how to avoid them
- How to generate and visualise your GAN’s first outputs
- A beginner-level introduction to image generation with a basic DCGAN
- Where to go next after completing this beginner course
Who is This Course For?
This course is for anyone who’s just getting started and wants a clear, friendly introduction to GANs:
- Complete beginners with no machine learning background
- Students exploring AI and generative models for the first time
- Python beginners who want their first real AI project
- Working professionals curious about how AI generates images and content
- Creative professionals – designers, artists, writers – exploring AI tools
- Career switchers taking their first step into the AI field
- Anyone who tried to learn GANs elsewhere and found it too complex
Tools and Technologies Covered
- Python 3.x (beginner-friendly explanations included)
- PyTorch (introduced gradually – no prior experience assumed)
- Google Colab (free GPU environment – nothing to install locally)
- NumPy (basics only, as needed)
- Matplotlib (for visualising GAN outputs)
- Jupyter Notebooks
Career Outcomes
Completing this beginner course plants the seeds for a strong AI career path. Here’s what it sets you up for:
- A solid foundation to progress to intermediate and advanced AI/ML courses
- Your first AI project to include in a portfolio or show to employers
- Readiness to pursue junior AI roles or AI-related internships
- Confidence to apply for entry-level Machine Learning Engineer or AI Analyst positions
- The base knowledge to specialise in computer vision, generative AI, or deep learning
- A globally recognised kodestree certificate to add to your LinkedIn and resume
- AI engineering is among the fastest-growing career paths in 2026, with strong entry-level demand across tech, healthcare, media, and retail
Why Choose kodestree for This Training?
Thousands of beginners have taken their first AI step with kodestree. Here’s what makes our beginner programme stand out:
- Live, instructor-led sessions – a real person explains, you ask questions, you learn
- Zero-to-code approach – we start from the absolute basics, no assumptions made
- Free Google Colab setup means no expensive GPU or local installation headaches
- Small batch sizes so beginners get personal attention, not just a video link
- Beginner-friendly capstone project included – you leave with something real
- Dedicated support channel to ask questions between sessions
- Placement guidance for students targeting entry-level AI roles
- Flexible batch timings – weekday and weekend options available
- Globally recognised course completion certificate
