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Machine Learning for Time Series Forecasting with Python
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment ...
Time Series Forecasting
Learn how to build and operationalize machine learning forecast models for your everyday projects. With this practical book, experienced and novice data scientists, business analysts, and AI developers will learn the steps necessary for building, training, and deploying time series forecasting models for their organizations. Time series data is an invaluable source of information used for future strategy and planning operations in several industries. From finance to education and health care, time series forecasting plays a major role in unlocking business insights with respect to time. During the past few decades, machine learning model-based forecasting has become popular in both the private and the public decision-making process.
Impact of Artificial Intelligence in Business and Society
Belonging to the realm of intelligent technologies, it is increasingly accepted that artificial intelligence (AI) has evolved from being merely a development standpoint in computer science. Indeed, recent reports and academic publications show that we are clearly on the path toward pervasive AI in both business and society. Organizations must adopt AI to maintain a competitive advantage and explore opportunities for unprecedented innovation. This book focuses on understanding the wide range of opportunities as well as the spectrum of challenges AI brings in different business contexts and society at large. The book highlights novel and high-quality research in data science and business analy...
Cyber Security and Business Intelligence
To cope with the competitive worldwide marketplace, organizations rely on business intelligence to an increasing extent. Cyber security is an inevitable practice to protect the entire business sector and its customer. This book presents the significance and application of cyber security for safeguarding organizations, individuals’ personal information, and government. The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security. It offers a roadmap to master degree students and PhD researchers for cyber security analysis in order to minimize the cyber security risk and protect ...
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The Business of Additive Manufacturing
Although additive manufacturing (AM), also known as 3D printing, has been around for almost 40 years, few people know how it actually works and the huge impact and benefits it offers. This book explains what AM is, using business theories to explain and illustrate why AM is increasingly being used across industries. The book translates complex engineering technology into relevant managerial terminology, using real-world examples from industries such as apparel, construction and transportation. It provides an introduction into the technical background of AM before expanding on the applications, opportunities and challenges to business models. Offering a unique managerial perspective, this book is aimed primarily at a scholarly audience and those researching across business disciplines, including technology management, manufacturing, production and operations management. It can also be used in emerging business courses on AM.
The Rise of Generative Artificial Intelligence
This timely book explores how generative artificial intelligence (GAI) is developing and diffusing, highlighting the diverse impacts this technology is likely to have on economies and societies. It also examines the effects on and the responses of industries where GAI has been the most pervasive.
Grassroots Innovation
This book explores the process of grassroots innovation in the context of the Global South. It explains why these bottom-up solutions developed by common people are generated due to a lack of available or affordable technology to meet their needs and how they are included in the mainstream imagination of the economy by studying these innovations in India. It analyses the grassroots innovation process from idea generation to its implementation. Detailing both theoretical and practical dimensions of grassroots innovation, the book provides a holistic understanding of the phenomenon by tracing its history in the pre-independence discourse on development to the present-day policies for instituti...
Practical MLOps
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Machine Learning for Time Series Forecasting with Python
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment ...
