Foundations of Artificial Intelligence with Python

Artificial intelligence is transforming how we solve complex problems, but getting started can feel overwhelming. This text-based course demystifies the core algorithms and mathematical concepts behind modern AI using clear explanations and practical Python examples. You will transition from an absolute beginner to a confident practitioner who understands how AI systems make decisions, search for solutions, and learn from data. By reading through structured explanations and analyzing real-world code snippets, you will master the foundational patterns that power search engines, recommendation systems, and predictive models. What you'll learn: - Understand foundational AI terminology, search algorithms, and knowledge representation techniques - Apply Python to build classic adversarial search models for game-playing scenarios - Implement machine learning algorithms for classification and regression tasks using modern libraries - Learn the basics of neural networks and deep learning architectures - Explore modern AI concepts including prompt engineering patterns and vector representations - Practice writing clean, type-hinted Python code to solve optimization and probability problems The course begins with essential terminology and the absolute basics of search algorithms before advancing to machine learning, probability, and neural networks. You will progress step-by-step through written explanations, conceptual breakdowns, and code-based exercises designed to reinforce your learning. This course is designed for beginners who have a basic familiarity with Python and want to learn how artificial intelligence works from the ground up, with no prior AI experience required. Start reading today to unlock the fundamental principles of artificial intelligence and build your Python AI toolkit.