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Principles of Machine Learning

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About this course

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

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/li>
  • Recommender systems
  • The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

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Course Syllabus

Explore classification
  • Understand the operation of classifiers
  • Use logistic regression as a classifier
  • Understand the metrics used to evaluate classifiers
  • Lab: Classification with logistic regression taught using Azure Machine Learning
Regression in machine learning
  • Understand the operation of regression models
  • Use linear regression for prediction and forecasting
  • Understand the metrics used to evaluate regression models
  • Lab: Predicting bike demand with linear regression taught using Azure Machine Learning
How to improve supervised models
  • Process for feature selection
  • Understand the problems of over-parameterization and the curse of dimensionality
  • Use regularization on over-parameterized models
  • Methods of dimensionality reduction Apply cross validation to estimating model performance
  • Lab: Improving diabetes patient classification using Azure Machine Learning
  • Lab: Improving bike demand forecasting using Azure Machine Learning
Details on non-linear modeling
  • Understand how and when to use common supervised machine learning models Applying ML models to diabetes patient classification
  • Applying ML models to bike demand forecasting Clustering
  • Understand the principles of unsupervised learning models
  • Correctly apply and evaluate k-means clustering models
  • Correctly apply and evaluate hieratical clustering model
  • Lab: Cluster models with AML, R and Python
Recommender systems
  • Understand the operation of recommenders
  • Understand how to evaluate recommenders
  • Know how to use alternative to collaborative filtering for recommendations
  • Lab: Creating and evaluating recommendations

Meet the instructors

Graeme Malcolm

Graeme Malcolm

Senior Content Developer Microsoft Learning Experiences

Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.

Dr. Steve Elston

Dr. Steve Elston

Managing Director Quantia Analytics, LLC

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

Cynthia Rudin

Cynthia Rudin

Associate Professor MIT and Duke

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.