What you'll learn
✓ Understand Artificial Intelligence, Machine Learning, and Deep Learning fundamentals
✓ Differentiate between AI vs Non-AI systems and real-world AI workloads
✓ Learn how machine learning models are trained, evaluated, and deployed
✓ Understand neural networks, including CNNs, RNNs, autoencoders, and deep learning
✓ Gain a clear overview of Generative AI and its applications across industries
✓ Learn how GANs, Diffusion Models, and Transformers work
✓ Understand tokens, embeddings, and transformer architecture
✓ Explore Large Language Models (LLMs) and small language models
✓ Learn about open-source, proprietary, and foundation models
✓ Understand fine-tuning vs training of AI models
✓ Explore AI agents, responsible AI principles, and GenAI challenges
✓ Get clarity on AI career paths and industry expectations

Requirements
● No prior experience in AI or Machine Learning is required
● Basic computer literacy is sufficient
● No programming knowledge is mandatory (concept-focused course)
● A curiosity to learn how modern AI and Generative AI systems work

Description
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.