AI and Machine Learning: Apply, Build, and Solve Problems

Artificial intelligence and machine learning are transforming how we solve complex problems, yet getting started can feel overwhelming with so many competing concepts. This structured, text-based course guides you step-by-step through the core theories, algorithms, and models that power modern intelligent systems. You will transition from understanding basic terminology to confidently designing intelligent agents, implementing search strategies, and working with modern data structures. By reading through clear explanations and analyzing structured code examples, you will build a solid theoretical and practical foundation in AI systems. What you'll learn: - Understand foundational AI terminology, intelligent agent architectures, and state-space representations. - Apply classic search algorithms and logical reasoning to solve complex decision problems. - Implement machine learning models for classification and regression tasks. - Configure rule-based expert systems using CLIPS to automate decision-making processes. - Apply probabilistic models and decision theory to handle uncertainty in data. - Explore modern AI patterns including vector databases and basic retrieval-augmented generation concepts. The course begins with essential definitions and the history of intelligent agents, ensuring you have the necessary context before moving into search algorithms and logical reasoning. You will then progress to machine learning fundamentals, expert systems, and contemporary retrieval patterns, reinforcing your knowledge with conceptual exercises and code walk-throughs. This course is designed for aspiring developers, data enthusiasts, and beginners who want a structured, text-focused introduction to AI and machine learning with no prior advanced mathematical background required. Start your journey into artificial intelligence and master the core algorithms that drive modern technology.