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This course provides a comprehensive introduction to Artificial Intelligence, Machine Learning, and Large Language Models (LLMs), designed to help students understand both the foundations and real-world applications of modern AI systems. Students will begin by learning the core concepts of Machine Learning, including data preprocessing, model training, evaluation techniques, and the principles behind intelligent decision-making systems. The course then dives deep into how Large Language Models work under the hood, covering topics such as tokenization, transformers, embeddings, attention mechanisms, and prompt engineering. Students will explore how these models power today’s most advanced AI systems and how developers can build practical applications on top of them. A major focus of the course is Retrieval Augmented Generation (RAG), where students will learn how to build intelligent AI systems that combine LLMs with external knowledge sources using vector databases, embeddings, and retrieval pipelines. Students will also learn fine-tuning techniques to adapt LLMs for specific domains and tasks. The course introduces modern Generative AI tools and frameworks such as LangChain, CrewAI, and agent-based AI systems, enabling students to design autonomous AI agents capable of planning, reasoning, and executing multi-step tasks. Students will learn how to orchestrate multiple agents that collaborate to solve complex problems, enabling powerful AI workflows and automation systems. Throughout the program, students will build hands-on AI applications including: AI Content Generators Retrieval-Augmented Generation (RAG) Chatbots Multimodal AI Assistants capable of handling text, images, and documents AI Agents that automate tasks and workflows Intelligent knowledge assistants powered by vector databases By the end of the course, students will have a strong understanding of modern AI architectures, LLM development, agentic AI systems, and production-ready AI applications. This course equips learners with the practical skills required to build next-generation AI products, GenAI applications, and intelligent automation systems used in industry today.
This course introduces the fundamentals of Machine Learning, including supervised and unsupervised learning, common algorithms, data preprocessing, and real-world use cases. By the end of the course, students will understand how machine learning models are built, trained, and evaluated.
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