This course focuses on Applied Machine Learning with a strong emphasis on Retrieval Augmented Generation (RAG). Students will learn practical machine learning techniques and how to build intelligent AI applications by combining LLMs with external knowledge using RAG architecture.

Motive

1. Master Applied Machine Learning concepts and techniques. 2. Learn to build Retrieval Augmented Generation (RAG) systems. 3. Develop end-to-end AI applications that combine ML and Generative AI. 4. Gain hands-on experience in implementing production-ready RAG solutions. 5. Prepare for roles in Machine Learning Engineering and AI Development.

Prerequisites

1. Basic to intermediate knowledge of Python. 2. Familiarity with basic Machine Learning concepts. 3. Understanding of linear algebra and statistics is beneficial. 4. Experience with Jupyter Notebook or VS Code. 5. No prior RAG experience required.

Course Content

Applied Machine Learning & RAG

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