This course provides a clear and structured explanation of artificial intelligence (AI) and its subfields, making complex concepts accessible for beginners. It starts by defining AI and exploring how machines simulate human intelligence to perform tasks such as reasoning, problem-solving, and decision-making.

The course then breaks down machine learning (ML), explaining how algorithms learn patterns from data to make predictions or decisions without explicit programming. Moving deeper, learners discover deep learning (DL), a subset of ML that uses neural networks to solve highly complex tasks, including image recognition, natural language processing, and autonomous systems.

Finally, the course introduces generative AI, highlighting how models like GPT, Llama, and other generative tools create new content from learned data, from text and images to code and music. Each section emphasizes practical examples and real-world applications, helping learners understand not only how these technologies work but also how they impact industries today.

By the end of the course, students will have a solid understanding of AI, ML, DL, and generative AI, preparing them for hands-on experimentation or further study in AI development