AI Engineering Foundations: Python, Machine Learning, and LLM Agents

The field of artificial intelligence is evolving rapidly, making it essential to master both the foundational mathematics and the latest implementation techniques. This course provides a structured path to understanding how AI works and how to build modern applications using industry-standard tools and practices. You will gain the skills needed to navigate the entire AI lifecycle, from data processing to model deployment. Through clear written explanations and code-based exercises, you will transform from a beginner into a capable practitioner ready to tackle complex data challenges. You will learn how to think like an AI engineer, focusing on logic, scalability, and ethical implementation. What you'll learn: - Understand the mathematical foundations of AI, including linear algebra, statistics, and probability. - Master Python programming for data science and manipulation using modern libraries. - Implement machine learning algorithms for classification, regression, and clustering. - Build and train deep learning models using neural networks and transformer architectures. - Apply Natural Language Processing (NLP) techniques to process text and build intelligent systems. - Explore modern AI agent frameworks and retrieval-augmented generation (RAG) patterns. - Practice MLOps fundamentals to manage the lifecycle and deployment of AI models. The curriculum begins with core definitions and Python basics before progressing through classical machine learning, deep learning, and advanced generative AI concepts. You will study written explanations and code examples that illustrate how to move from theory to functional AI systems. This course is designed for beginners with no prior AI experience; a basic interest in logic and data is all you need to start. Start your journey into the world of artificial intelligence today.