Getting Started with Microsoft Azure Machine Learning Buck Woody | Senior Technical Specialist, Microsoft Scott Klein | Senior Technical Evangelist, Microsoft Seayoung Rhee | Senior Technical Product Manager, Microsoft

Meet Buck Woody | ‏@BuckWoody Senior Technical Specialist, Microsoft Corporation Instructor, University of Washington, Data Science Board Over 30 years of data and teaching experience Frequent Speaker at various conferences worldwide

Meet Seayoung Rhee | ‏@seayoungrhee Senior Technical Product Manager, Microsoft Machine Learning and Advanced Analytics technical marketing

Meet Scott Klein | ‏@SQLScott Senior Technical Evangelist, Microsoft Corporation Frequent Speaker at various conferences worldwide

Course Topics Getting Started with AzureML 01 | An Introduction to Machine Learning and AzureML Studio 02 | Designing a Recommender Solution with AzureML 03 | Monetizing your AzureML Application with the Microsoft Azure Marketplace 04 | AzureML API Services and Extensibility Scenarios

Setting Expectations Target Audience Data Professional Developer Business Intelligence Professional Suggested Prerequisites/Supporting Material Programming Probability / Statistics Calculus Linear Algebra

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01 | An Introduction to Machine Learning and AzureML Studio Buck Woody | Senior Technical Specialist, Microsoft Seayoung Rhee | Senior Technical Product Manager, Microsoft

Module Overview Machine Learning Overview AzureML Strengths Microsoft Azure Overview, Getting an Account and Using Storage Accounts Setting up an AzureML Workspace Exploring AzureML Studio

Machine Learning / Predictive Analytics Server & Tools Business 12/9/2019 Machine Learning / Predictive Analytics Machine learning & predictive analytics are core capabilities that are needed throughout your business Vision Analytics Recommenda-tion engines Advertising analysis Weather forecasting for business planning Social network analysis Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location-based tracking and services Personalized Insurance © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Machine Learning Overview Formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E” - Tom M. Mitchell Another definition: “The goal of machine learning is to program computers to use example data or past experience to solve a given problem.” – Introduction to Machine Learning, 2nd Edition, MIT Press ML often involves two primary techniques: Supervised Learning: Finding the mapping between inputs and outputs using correct values to “train” a model Unsupervised Learning: Finding patterns in the input data (similar to Density Estimates in Statistics)

Machine Learning A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Data: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Rules, or Algorithms: about, Learning, language – Spelling and sounding builds words Learning about language. – Words build sentences Learning, or Abstraction: Any new understanding proceeds from previous knowledge.

Supervised Learning Used when you want to predict unknown answers from answers you already have – requires data which shows the answers you can get now Data is divided into two parts: the data you will use to “teach” the system (data set), and the data you will use to see if the computer’s algorithms are accurate (test set) After you select and clean the data, you select data points that show the right relationships in the data. The answers are “labels”, the categories/columns/attributes are “features” and the values are…values. Then you select an algorithm to compute the outcome. (Often you choose more than one) You run the program on the data set, and check to see if you got the right answer from the test set. Once you perform the experiment, you select the best model. This is the final output – the model is then used against more data to get the answers you need

Supervised Learning Car Not Car

Unsupervised Learning Used when you want to find unknown answers – mostly groupings - directly from data No simple way to evaluate accuracy of what you learn Evaluates more vectors, groups into sets or classifications Start with the data Apply algorithm Evaluate groups

Unsupervised Learning Example 1 example A Example 2 example B Example 3 example C example A example B example C Example 1 Example 2 Example 3

AzureML Why AzureML? Setting up a Microsoft Azure Account Setting up a Storage Account Loading Data Setting up an AzureML Workspace Accessing AzureML Studio AzureML Studio Tour