Principles of Machine Learning

About This Course

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.

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 instructor

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

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

Associate Professor

MIT and Duke