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Principles of Machine Learning

About This Course

This course is part of the Microsoft Professional Program Certificate in Data Science.

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, Python, and Azure Machine Learning.

What you'll learn

  • Explore classification
  • Regression in machine learning
  • How to improve supervised models
  • Details on non-linear modeling
  • Clustering
  • Recommender systems
  • The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

Meet the instructors

Course Staff Image #1

Dr. Steve Elston

Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies

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Cynthia Rudin

Cynthia leads the Prediction Analysis Lab at MIT, and is associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management. She holds a PhD in applied and computational mathematics from Princeton University, and was previously, an associate research scientist at the Center for Computational Learning Systems at Columbia U.

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