AI Search Algorithms and Problem Solving for Beginners

Finding efficient paths and making optimal decisions are at the heart of artificial intelligence. This written course guides you through the core search algorithms that power modern AI problem-solving, from basic pathfinding to complex strategic systems. You will transition from understanding simple state-space graphs to implementing advanced heuristic search techniques. By reading through clear, step-by-step explanations and structured code snippets, you will gain the conceptual clarity needed to model and solve real-world optimization problems. What you'll learn: Understand state-space representation and formulation of AI problems; Master uninformed search techniques including Breadth-First and Depth-First Search; Apply informed heuristic strategies like Greedy Best-First and A* Search; Implement adversarial search algorithms including Minimax and Alpha-Beta Pruning; Explore modern search applications such as Monte Carlo Tree Search used in advanced gaming AI; Analyze algorithm complexity and performance to choose the right strategy for any problem. This course begins with foundational definitions of state spaces and search trees before moving into practical algorithm design. You will study structured written walkthroughs and pseudocode examples that illustrate how these algorithms function in practice. This course is designed for beginners in computer science, software development, and data science who want to build a strong algorithmic foundation in AI. No prior background in artificial intelligence is required, though basic programming logic is helpful. Start reading today to master the core search algorithms that drive intelligent decision-making.