This episode of Techsplainers explores supervised learning, the most widely used approach in machine learning, where AI models are trained using labeled data with known correct answers. The episode explains how supervised learning uses ground truth data to teach models to recognize patterns and make accurate predictions on new information. We break down the two main categories of supervised learning tasks—classification for sorting data into categories and regression for predicting numerical values—and examine popular algorithms, including linear regression, decision trees, random forests, and neural networks. The discussion also covers how supervised learning differs from other approaches like unsupervised, semi-supervised, self-supervised, and reinforcement learning, along with real-world applications ranging from image recognition to fraud detection. While highlighting supervised learning's effectiveness for many AI applications, the episode acknowledges its limitations, including data labeling requirements and potential for bias.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska