Introduction to Machine Learning in Wolfram Language

This course introduces some of the basic concepts of machine learning as well as easy-to-use machine learning superfunctions available in Wolfram Language. You will learn how to perform supervised and unsupervised learning tasks with just a few lines of code. We will start with examples of different types of machine learning and then move on to more details on performing machine learning with labeled and unlabeled data. Examples demonstrate regression, classification, clustering and anomaly detection. Basic familiarity with Wolfram Language or introductory-level skill in any programming language is recommended.

Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)

Outline

• What Is Machine Learning?: Learn about common machine learning terms. See examples of built-in Wolfram Language functions that can perform a variety of machine learning tasks. Explore common paradigms of machine learning as well as their variations.
• Machine Learning Workflows: Learn about traditional machine learning workflows where you get data and then build, train, test and deploy a model. Also get a peek at newer workflows that incorporate LLMs (large language models).

• Supervised Learning: Understand how one of the most popular machine learning paradigms works. Use Wolfram Language superfunctions Classify and Predict with labeled data. Handle common issues with training data.
• Unsupervised Learning: Use unsupervised learning to work with unlabeled data. Use Wolfram Language functions like FeatureExtract, FindClusters and ClusterClassify. Detect anomalies in data and model the distribution of non-anomalous data.