Machine Learning & LLMs
Machine learning, neural networks and large language models (LLMs) are important components of modern AI systems. Learn about popular machine learning paradigms for classification, regression, clustering and anomaly detection with the help of fully automated and customizable functions that handle everything from feature extraction to performance evaluation. See how you can select pre-trained neural net models from a repository to apply to your own data, customize existing models or build models from scratch with the help of a symbolic neural net framework. Make use of Chat Notebooks as well as powerful built-in functions for calling LLM functionality and allowing LLMs to access Wolfram Language tools.
These courses cover many different topics, starting with introductory machine learning concepts and Wolfram Language built-in functions and diving into the complexities of building and training neural networks. Earn course completion certificates and prepare for Wolfram Language Level 1 certification.
Upcoming Events
-
Jan 15 | Online
Introduction to Neural Networks in Wolfram Language
This course provides the knowledge, tools and guidance to efficiently create and maintain Wolfram Language projects. Build on your existing programming skills with a quick review of basic syntax and then develop a deeper understanding of patterns, interactive interfaces and cloud deployment. Learn about coding best practices, including error handling and the use of IDEs. This course is designed for Wolfram Language users who are ready to move beyond the fundamentals and create their own programs and interfaces.
-
Jan 16 | Online
Turn Rough Ideas into Computation with Wolfram Notebook Assistant
Learn how to turn conversational input into precise computational code for accessing the full power of Wolfram Language. This webinar demonstrates ways you can interact with Wolfram Notebook Assistant, showcases examples for writing and fixing code and explains how to take advantage of LLM functionality via Wolfram Language functions.
-
Jan 29 | Online
Big Problems with Big Data: Managing Risks in AI
This talk explores the issues you need to consider in making data-driven decisions. It discusses topics such as when machine learning is appropriate, sources of bias, validation and explainability of models and decision-making criteria.
