Machine Learning In Business

Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it). Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. About the book Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results. What's inside Identifying tasks suited to machine learning Automating back office processes Using open source and cloud-based tools Relevant case studies About the reader For technically inclined business professionals or business application developers. About the author Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size. Table of Contents: PART 1 MACHINE LEARNING FOR BUSINESS 1 ¦ How machine learning applies to your business PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS 2 ¦ Should you send a purchase order to a technical approver? 3 ¦ Should you call a customer because they are at risk of churning? 4 ¦ Should an incident be escalated to your support team? 5 ¦ Should you question an invoice sent by a supplier? 6 ¦ Forecasting your company’s monthly power usage 7 ¦ Improving your company’s monthly power usage forecast PART 3 MOVING MACHINE LEARNING INTO PRODUCTION 8 ¦ Serving predictions over the web 9 ¦ Case studies

"The big data revolution is changing the way businesses operate and the skills required by managers. In creating the third edition, John Hull has continued to improve his material and added many new examples. The book explains the most popular machine learning algorithms clearly and succinctly; provides many examples of applications of machine learning in business; provides the knowledge managers need to work productively with data science professionals; has an accompanying website with data, worksheets, and Python code"--Back of cover.

This book teaches the full process of how to conduct machine learning in an organizational setting. It develops the problem-solving mind-set needed for machine learning and takes the reader through several exercises using an automated machine learning tool. To build experience with machine learning, the book provides access to the industry-leading AutoML tool, DataRobot, and provides several data sets designed to build deep hands-on knowledge of machinelearning.

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks, cloud-based systems (e.g., Salesforce), databases (e.g., Oracle), AWS machine learning (e.g., Amazon Lex), and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies, this book provides a working knowledge of AI’s current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing, banking, finance and education. It is essential reading for anyone involved in industry.

This book is for business executives and students who want to learn about the tools used in machine learning. In creating the second edition, John Hull has continued to improve his material and added three new chapters. The book explains the most popular algorithms clearly and succinctly without using calculus or matrix/vector algebra. The focus is on business applications. There are many illustrative examples. These include assessing the risk of a country for international investment, predicting the value of real estate, and classifying retail loans as acceptable or unacceptable. Data, worksheets, and Python code for the examples is on the author's website. A complete set of PowerPoint slides that can be used by instructors is also on the website. The opening chapter reviews different types of machine learning models. It explains the role of the training data set, the validation data set, and the test data set. It also explains the issues involved in cleaning data and reviews Bayes' theorem. Chapter 2 is devoted to unsupervised learning. It explains the k-means algorithm and alternative approaches to clustering. It also covers principal components analysis. Chapter 3 explains linear and logistic regression. It covers regularization using Ridge, Lasso, and Elastic Net. Chapter 4 covers decision trees. It includes a discussion of the naive Bayes classifier, random forests, and other ensemble methods. Chapter 5, explains how the SVM approach can be used for both linear and non-linear classification as well as for the prediction of a continuous variable. Chapter 6 is devoted to neural networks. It includes a discussion of the gradient descent algorithm, backpropagation, stopping rules, autoencoders, convolutional neural networks, and recurrent neural networks. Chapter 7 explains reinforcement learning using two games as examples. It covers Q-learning and deep Q-learning, and discusses applications. Chapter 8 covers natural language processing. It discusses how the algorithms introduced in the book can be used for sentiment analysis, language translation and information retrieval. Chapter 9 is concerned with model interpretability. It discusses the importance of making models understandable and the procedures that can be used for both white-box and black-box models. Chapter 10 explains two applications involving derivatives that the author has been involved in. The final chapter focuses on issues for society. The topics covered include data privacy, biases, ethical considerations, legal issues, and adversarial machine learning. At the ends of chapters there are short concept questions to test the readers understanding of the material and longer exercises. Answers are at the end of the book. The book includes a glossary of terms and an index.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.