Overview

Machine Learning Proficiency: Wolfram U Instructor-Led Course

This instructor-led course provides an introduction to machine learning, neural networks, and LLMs, followed by hands-on projects using Wolfram technology. Learn how to build, train, and test models in this comprehensive, three-part course sequence that leverages the computational power of Wolfram technologies to establish reliable AI systems.

Course Sequence:

• Introduction to Machine Learning: Understand essential concepts of machine learning and explore the easy-to-use machine learning superfunctions available in Wolfram Language.
• Introduction to Neural Networks: Discover the state-of-the-art Neural Net Framework in Wolfram Language and utilize the Wolfram Neural Net Repository for prebuilt and pretrained models.
• Wolfram Language and LLMs: Explore the application of LLMs with Wolfram Language, including using the conversational interface of Chat Notebooks and leveraging programmatic operations with LLM functions.

Each course can be taken individually, but enrolling in the full course sequence provides a cohesive schedule and access to exclusive office hour sessions.

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 and their variations.
• Explore popular techniques like neural networks for deep learning.
• Access the power of LLMs in various ways for modern machine learning workflows.

Machine Learning Workflows

• Obtain data from different external and built-in sources.
• Build, train, and test models according to traditional machine learning workflows.
• Utilize built-in metrics to evaluate model performance on test data.
• Deploy models quickly using Wolfram Cloud services.
• Access LLM models within Wolfram Language to enhance workflows with modern AI tools.

Use Wolfram Tech

• Employ built-in machine learning superfunctions like Classify, Predict, FindClusters, and ClusterClassify, as well as LLM functions such as LLMFunction, LLMExampleFunction, and LLMSynthesize.
• Utilize 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

• Engage in practical examples demonstrating regression, classification, clustering, and anomaly detection.
• Construct simple neural networks and apply transfer learning.
• Create fun and functional LLM prompts.

University: Wolfram U

Provider: Wolfram U

Categories: Artificial Intelligence Courses, Machine Learning Courses, Neural Networks Courses, Classification Courses, Wolfram Language Courses

Syllabus

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