The Business of Machine Learning

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Successfully and proactively take charge of your machine learning strategy. Machine learning (ML) is permeating every sector and aspect of business, from evaluating the success of a massive online marketing campaign, to predicting insurance payouts, to crime scene analysis. This book shows how to interpret patterns and redundancies from massive amounts of existing data to help your business cut costs, operate more efficiently and effectively, and get to the next level. You will learn how to analyze, communicate, and launch a viable program that, when done correctly, will positively transform your business. The authors engage you to experience the business of machine learning through actual conversations that open with an exchange between a data scientist and and his counterpart in business, the technical decision maker. You will learn where to go when the conversation leads to an impasse and work step-by-step to methodically resolve the challenges. After reading this book, you will come away with the confidence to tackle a machine learning strategy customized for your team or business objectives. Revel in the vast capabilities of machine learning tools at your disposal and reach that "a ha" moment when you discover the profound and enduring impact machine learning can have on your business. What You'll Learn:Understand the vast potential of machine learning, and how and when to apply different ML techniquesDevise strategies to improve efficiency and accuracy in your businessGet to know your customers and their specific needs through interpreting highly accurate and complex dataCommunicate more effectively with teams of architects and data scientists as they develop and deploy complex machine learning solutions Contrast the life cycle of a machine learning project to a software development project Master terms such as “convolutional neural network, ” “nonparametric regression, ” and “multi-class decision jungle”This book is for any technical business decision maker who has to implement a machine learning strategy or converse with data scientists. A basic level of technical understanding is helpful, but does not have to be specific to programming languages or operating systems.This book is open access under a CC BY license.​Josh Holmes is CTO of the Commercial Software Engineering Americas team at Microsoft. Prior to joining Microsoft, Josh consulted for a variety of clients ranging from large Fortune 500 firms to startups. Josh speaks and presents globally on the topics of IoT and machine learning. A tireless and passionate advocate for the tech community, Josh has founded and/or run numerous organizations, including the Great Lakes Area .NET Users Group and the Ann Arbor Computer Society. Mike Lanzetta designs and implements machine earning solutions for Fortune 500 companies at Microsoft. He has been doing software development for more than 20 years, working at four-person startups to Amazon. Mike regularly presents and chairs at conferences nationally and internationally. He has an M.Sc. in CSE from UW and a B.Sc. in CE from UCSC. He is often found blogging or tweeting on the topic of machine learning.