Bandit Algorithms and Online Machine Learning for Beginners

How do systems make optimal choices when faced with limited, real-time feedback? Bandit algorithms are the foundation of modern recommendation engines, dynamic pricing, and A/B testing, enabling systems to learn and adapt on the fly. This course provides a clear, text-based introduction to sequential decision-making, taking you from foundational probability concepts to practical online learning algorithms. You will transition from understanding basic exploration-exploitation dilemmas to writing clean, algorithmic logic that optimizes rewards in real-time environments. What you'll learn: - Understand the core tension between exploration and exploitation in online learning - Implement multi-armed bandit strategies including Greedy, Epsilon-Greedy, and Upper Confidence Bound algorithms - Analyze regret bounds to measure the efficiency and performance of your decision-making models - Explore Thompson Sampling and Bayesian approaches to sequential optimization - Apply contextual bandit concepts to simulate personalized recommendation systems - Practice evaluating online learning models using simulated environmental feedback We begin with essential definitions, probability basics, and core terminology before moving systematically through classic algorithms, mathematical bounds, and practical implementation scenarios. This step-by-step progression ensures you build a strong conceptual and practical foundation. This course is designed for aspiring data scientists, software engineers, and machine learning enthusiasts who want to learn online learning principles from scratch. No prior experience with reinforcement learning is required, though basic Python familiarity will help you get the most out of the code examples. Start reading today to master the algorithms that power modern real-time decision systems.