Instructor-led course gives an intro to machine learning, neural networks and LLMs, then jumps into hands-on projects using Wolfram tech. How to build, train and test models.
This three-part course sequence will guide you in using the computational power of Wolfram technologies as a foundation for reliable AI systems. First, learn about the basic concepts of machine learning as well as the easy-to-use machine learning superfunctions available in Wolfram Language. In the second course, you'll learn about the state-of-the-art Neural Net Framework in Wolfram Language and how to explore the Wolfram Neural Net Repository for prebuilt and pretrained models. The third course in the sequence shows you different ways you can use LLMs with Wolfram Language, including how to use the conversational interface of Chat Notebooks and the programmatic operations possible with LLM functions. Each course is available individually, but the course sequence gives you the convenience of a unified schedule and an invitation to special office hour sessions. The series includes three machine learning courses: Introduction to Machine Learning in Wolfram Language, Introduction to Neural Networks in Wolfram Language and Wolfram Language and LLMs.
Featured Products & Technologies: Wolfram Language and Wolfram Notebooks (available in Mathematica, Wolfram|One and Wolfram|Alpha Notebook Edition), Neural Net Repository
Outline
What Is Machine Learning?: Learn about common machine learning paradigms as well as their variations. Explore popular techniques like neural networks for deep learning. Access the power of LLMs in different ways for modern machine learning workflows.
Machine Learning Workflows: Get data from different external and built-in sources. Build, train and test models according to traditional machine learning workflows. Use built-in metrics to evaluate the performance of models on test data. Quickly deploy a model for use with the help of the Wolfram Cloud. See how to access LLM models from within Wolfram Language to augment your workflow with modern AI tools.
Use Wolfram Tech: Use built-in machine learning superfunctions like Classify, Predict, FindClusters and ClusterClassify as well as LLM functions like LLMFunction, LLMExampleFunction and LLMSynthesize. Learn about using prebuilt and pretrained neural net models from the Neural Net Repository. Work with LLM models both programmatically and interactively with chat-based access and a curated collection of prompts from the Wolfram Prompt Repository.
Explore Hands-on Examples: Work on easy-to-apply practical examples that demonstrate regression, classification, clustering and anomaly detection. Build simple neural networks and also apply transfer learning. Write fun and functional LLM prompts.
This three-part course sequence will guide you in using the computational power of Wolfram technologies as a foundation for reliable AI systems. First, learn about the basic concepts of machine learning as well as the easy-to-use machine learning superfunctions available in Wolfram Language. In the second course, you'll learn about the state-of-the-art Neural Net Framework in Wolfram Language and how to explore the Wolfram Neural Net Repository for prebuilt and pretrained models. The third course in the sequence shows you different ways you can use LLMs with Wolfram Language, including how to use the conversational interface of Chat Notebooks and the programmatic operations possible with LLM functions. Each course is available individually, but the course sequence gives you the convenience of a unified schedule and an invitation to special office hour sessions. The series includes three machine learning courses: Introduction to Machine Learning in Wolfram Language, Introduction to Neural Networks in Wolfram Language and Wolfram Language and LLMs.
Featured Products & Technologies: Wolfram Language and Wolfram Notebooks (available in Mathematica, Wolfram|One and Wolfram|Alpha Notebook Edition), Neural Net Repository
Outline
What Is Machine Learning?: Learn about common machine learning paradigms as well as their variations. Explore popular techniques like neural networks for deep learning. Access the power of LLMs in different ways for modern machine learning workflows.
Machine Learning Workflows: Get data from different external and built-in sources. Build, train and test models according to traditional machine learning workflows. Use built-in metrics to evaluate the performance of models on test data. Quickly deploy a model for use with the help of the Wolfram Cloud. See how to access LLM models from within Wolfram Language to augment your workflow with modern AI tools.
Use Wolfram Tech: Use built-in machine learning superfunctions like Classify, Predict, FindClusters and ClusterClassify as well as LLM functions like LLMFunction, LLMExampleFunction and LLMSynthesize. Learn about using prebuilt and pretrained neural net models from the Neural Net Repository. Work with LLM models both programmatically and interactively with chat-based access and a curated collection of prompts from the Wolfram Prompt Repository.
Explore Hands-on Examples: Work on easy-to-apply practical examples that demonstrate regression, classification, clustering and anomaly detection. Build simple neural networks and also apply transfer learning. Write fun and functional LLM prompts.
