1. This document and the information contained herein is confidential and proprietary to Allegient LLC and shall not be duplicated, used or disclosed in whole or in part for any purpose other than review. All trademarks and/or service marks contained within this document are the property of their respective owners. Allegient does not in any way warrant the use of their products and/or services offerings. WHAT IS THIS AND HOW CAN I USE IT?  2016 Allegient. All rights reserved. Warren Sifre

2. Managing Consultant at Allegient. In the IT Industry since 1998. Developed system integration solutions against many different database platforms. Passion in Solutions Architecture at both hardware and software levels. Interests in SQL Server, MongoDB, Hadoop, Python/C#/Java/Powershell and Information Security (Hacking)  2016 Allegient. All rights reserved.

3. What is Machine Learning? Techniques of Learning What is Azure ML? Demonstration  2016 Allegient. All rights reserved.

5. “In general, ML converts data sets into pieces of software, known as ‘models’, that can represent the data set and generalize to make predictions on new data.” - John Platt http://blogs.technet.com/b/machinelearning/archive/2014/07/01/what-is-machine-learning.aspx 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, 2 nd Edition, MIT Press

6. Supervised o Learning by using a set number of inputs and outputs. o Limited in providing deep undiscovered insights. o Used when you want to predict unknown answers from answers you already have. (Historical Data) Unsupervised o Learning by using all inputs. o Can learn larger and more complex models. o Can take longer to learn depending on the number of linear levels. o Used when you want to find unknown answers directly from data. (No simple way to validate results.)  2016 Allegient. All rights reserved.

7. A cloud-based platform for designing, developing, testing and deploying predictive models. There are three conceptual areas Experiments, Web Service and ML Studio. o Experiments –Predictive models for both training and scoring. o Web Service – Data Interaction point for predictive models. o ML Studio – Interface for developing and training predictive models. Leverage existing R, Python, OpenCV and other industry adopted predictive algorithms.  2016 Allegient. All rights reserved.

8. http://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-cheat-sheet/

9.  2016 Allegient. All rights reserved.

10. Azure ML Studio o https://studio.azureml.net/ https://studio.azureml.net/ Azure ML – Microsoft Virtual Academy o http://www.microsoftvirtualacademy.com/training-courses/getting-started-with- microsoft-azure-machine-learning http://www.microsoftvirtualacademy.com/training-courses/getting-started-with- microsoft-azure-machine-learning Azure ML Algorithm Cheat Sheet o http://azure.microsoft.com/en-us/documentation/articles/machine-learning- algorithm-cheat-sheet/ http://azure.microsoft.com/en-us/documentation/articles/machine-learning- algorithm-cheat-sheet/  2016 Allegient. All rights reserved.

11. Warren Sifre Email: wsifre@allegient.com Twitter: @WAS_SQL LinkedIn: www.linkedin.com/in/wsifre