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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Dec 20 | Online
Build Custom Neural Networks
This talk introduces the practical steps needed to use or retrain neural networks and the basics of creating your own. No prior knowledge beyond basic coding skills is assumed. Examples include computer vision, sequence prediction and classification.
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JAN 6–17 | Online
Wolfram Neural Networks Boot Camp
Learn about neural networks, how to use pre-trained networks and how to build and train your own models in this two-week online boot camp. Interact with Wolfram experts; explore ways that AI and deep learning can be applied to text, image and audio analysis; and earn Wolfram certifications.
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Jan 8–22 | Online
Exploring AI Foundations with Wolfram Tools
This three-part course sequence guides you in using the computational power of Wolfram technologies as a foundation for reliable AI systems. Discover concepts in machine learning, explore the Neural Net Repository and learn to use LLMs.
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Jan 15 | Online
Statistics and Machine Learning: Better Together
This example-driven exploration showcases how the symbolic nature of Wolfram Language makes the handling of statistical distributions simple, how automation makes machine learning accessible and how the two fields together can utilize powerful and flexible tools.
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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.
