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
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NOV 10–14 | Online
Wolfram Language and LLMs: Ideal Complements
Join this study group to learn how to use LLMs within and in combination with Wolfram Language. This series begins with simple notebook chats, then explores integrating LLMs into workflows, building custom functions and saving time with automation. Later sessions will demonstrate how Wolfram Language computation can make LLM responses more reliable and explore methods for combining LLM agents and Wolfram Language code for parallel execution.
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Nov 11 | Online
Getting Started with AI: A Beginner's Guide to Automated Classification, Predictions and Computer Vision
This webinar explains the basics of supervised and unsupervised machine learning in Wolfram Language using illustrative examples in a range of subjects.
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NOV 13 | Online
Wolfram Language and LLMs
This instructor-led course will show you different ways you can use LLM technology alongside Wolfram Language, including how to use the conversational interface of Wolfram Notebook Assistant, Chat Notebooks and the programmatic operations possible with built-in LLM functions. Write code that interfaces with different service providers' LLM models from within the Wolfram environment as well as provides symbolic representation of tools that can be used by LLMs.
