Artificial Intelligence Fundamentals: Problem Solving and Modern AI Patterns

Understanding the core mechanisms of artificial intelligence is the first step toward building systems that can reason, learn, and solve complex problems. This course demystifies the foundational theories of AI and connects them to modern workflows shaping the industry today. By reading through clear, written explanations and engaging with structured code exercises, you will transition from a curious learner to a practitioner capable of conceptualizing intelligent systems. You will learn how classic search and knowledge representation methods lay the groundwork for modern machine learning patterns and generative AI technologies. What you'll learn: 1. Understand fundamental AI terminology, search algorithms, and knowledge representation models. 2. Apply classic problem-solving strategies to resolve structured computational challenges. 3. Explore the basics of machine learning, neural networks, and modern transformer architectures. 4. Discover contemporary AI concepts, including prompt engineering and retrieval-augmented generation (RAG). 5. Analyze how computational systems process language and vision to mimic human intelligence. 6. Practice writing clean, foundational AI algorithm structures using clear text-based code examples. The course begins with essential terminology and the historical foundations of symbolic AI before progressing to modern machine learning workflows and contemporary generative AI patterns. You will follow a logical progression from abstract problem-solving paradigms to concrete computational implementations. This course is designed for absolute beginners, aspiring developers, and tech enthusiasts with no prior background in artificial intelligence or advanced mathematics. Start reading today to unlock the core principles behind the AI technologies of yesterday, today, and tomorrow.