Learn about Machine Learning patterns and use cases in the real world, while getting a review of statistics and data analytics to be more data-minded, and helping to understand the Data Science process.
After this course, you will be able to:
- Describe the role of Machine Learning and where it fits into Information Technology strategies
- Explain the technical and business drivers that result from using Machine Learning
- Describe Supervised and Unsupervised learning techniques and usages
- Understand techniques like Classification, Clustering, and Regression
- Discuss how to identify which kinds of technique to be applied for a specific use case
Topic Outline:
- Course Introduction
- History and background of Machine Learning
- Compare Traditional Programming Vs Machine Leaning
- Supervised and Unsupervised Learning Overview
- Machine Learning patterns
- Classification
- Clustering
- Regression - Gartner Hype Cycle for Emerging Technologies
- Machine Learning offerings in Industry
- Hands-on exercise: Apply the Machine Learning Basics to real-life use case
- References and Next steps
University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.
