This article presents a collection of mind maps that illustrate key machine learning (ML) concepts and algorithms. Where relevant, it highlights commonly used ML libraries, packages, classes, and functions—with a particular focus on components from the Scikit-learn library.

We have previously explored two multi-armed bandit (MAB) strategies: Maximum Average Reward (MAR) and Upper Confidence Bound (UCB). Both approaches rely on the observed average reward to determine which arm to pull next, using a deterministic scoring mechanism for decision-making.

In this article, I will explore the balance between exploration and exploitation, a key concept in reinforcement learning and optimization problems. To illustrate this, I will use the multi-armed bandit problem as an example. I will also explain how the epsilon-greedy strategy effectively manages this balance.