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Principles of Machine Learning: R Edition

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  • Description
  • Unit Outline
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  • Additional information
  • Certificate
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Description

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, and Azure Notebooks.

What you’ll learn

After completing this course, you will be familiar with the following concepts and techniques:

  • Data exploration, preparation and cleaning
  • Supervised machine learning techniques
  • Unsupervised machine learning techniques
  • Model performance improvement

Prerequisites

To complete this course successfully, you should have:

  • A basic knowledge of math
  • Some programming experience – R is preferred.

Estimate Time : 12 hours

Module 1 Introduction to Machine Learning

  • High level Data Science Process
  • Overview Machine Learning
  • Lab

Module 2 Exploring Data

  • Exploratory Data Analysis for Regression
  • Exploratory Data Analysis for Classification
  • Lab

Module 3 Cleaning and Preparing Data

  • Data Preparation and Cleanning
  • Feature Engineering
  • Lab

Module 4 Getting Started with Supervised Learning

  • Regression
  • Classification
  • Lab

Module 5 Improved Model Performance

  • Principles of Model Improvements
  • Techniques for Improving Models
  • Dimensionality Reduction
  • Lab

Module 6 Machine Learning Algorithms

  • Introduction to Decision Trees
  • Ensemble Methods
  • Neural Networks
  • Support Vector Machines
  • Bayes Theorem
  • Lab

Module 7 Unsupervised Learning

  • Clustering
  • Lab


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

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.


Johnathan Sanito
Senior Content Developer
Microsoft

Jonathan works as a content developer and project manager for Microsoft focusing in Data and Analytics online training. He has worked with trainings for developer and IT pro audiences, from Microsoft Dynamics NAV to Windows Active Directory.

Before coming to Microsoft, Jonathan worked as a consultant for a Microsoft partner, implementing Microsoft Dynamics NAV solutions.

Additional information

Author / Publisher

Microsoft

Level

Intermediate

Language

English

Certificate

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