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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
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
Module 2 Exploring Data
- Exploratory Data Analysis for Regression
- Exploratory Data Analysis for Classification
Module 3 Cleaning and Preparing Data
- Data Preparation and Cleanning
- Feature Engineering
Module 4 Getting Started with Supervised Learning
Module 5 Improved Model Performance
- Principles of Model Improvements
- Techniques for Improving Models
- Dimensionality Reduction
Module 6 Machine Learning Algorithms
- Introduction to Decision Trees
- Ensemble Methods
- Neural Networks
- Support Vector Machines
- Bayes Theorem
Module 7 Unsupervised Learning
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
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.
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.
Senior Content Developer
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.
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