Generative AI is rapidly transforming how software, products, and businesses are built across industries. This course is designed to give you a clear, structured, and beginner-friendly introduction to Artificial Intelligence, Machine Learning, Neural Networks, and Generative AI, without overwhelming you with heavy mathematics or complex coding.
You will begin by understanding the fundamentals of AI, including different types of AI systems, real-world AI workloads, and common industry use cases. From there, the course introduces the core concepts of Machine Learning—how algorithms and models work, how models are trained, evaluated, and improved, and how machine learning fits into modern AI workflows and career paths.
As you progress, you’ll explore the foundations of neural networks and deep learning, including perceptrons, fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and variational autoencoders. These topics are explained conceptually to help you understand how modern AI systems actually function.
The course then moves into Generative AI, covering key model families such as Generative Adversarial Networks (GANs), diffusion models, and transformers. You’ll learn essential concepts like tokens, embeddings, transformer architecture, and how Large Language Models (LLMs) are built, trained, and fine-tuned. You will also explore popular open-source and proprietary models, AI agents, responsible AI principles, and the challenges associated with deploying generative AI systems.
Throughout the course, quizzes and real-world examples reinforce your understanding and help you assess your progress. By the end of this course, you will have a strong conceptual foundation in Generative AI and the confidence to explore advanced tools, roles, or hands-on learning paths in the AI ecosystem.
