This tutorial explores three major categories of machine learning algorithms: supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data, meaning the desired output is known, and the model learns to map inputs to outputs. An example is training a model to identify animal images. Unsupervised learning, on the other hand, works with unlabeled data, where the model discovers patterns and features without predefined outputs. A case in point is classifying vehicles (like buses or trucks) based on learned features like length and width. Reinforcement learning involves training a model to make decisions in an environment through trial and error, learning from rewards and penalties. This is commonly used in game playing and robotics.