Machine Learning for Dummies by
One of Mark Cuban's top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn't quite mean you can create your own Turing Test-proof android--as in the movie Ex Machina--it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models--and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying--and fascinating--math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.
Handbook of Machine Learning by
This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.
Machine Learning by
AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author--an expert in the field--presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection-- essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years' experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.
Artificial Intelligence: change management
How AI Is Transforming the Organization by
A clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. Descriptions of AI's possible effects on businesses and their employees cycle between utopian hype and alarmist doomsaying. This book from MIT Sloan Management Review avoids both these extremes, providing instead a clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. The contributors show that organizations can create business value with AI by cooperating with it rather than relinquishing control to it. The smartest companies know that they don't need AI that mimics humans because they already have access to resources with human capability--actual humans. The book acknowledges the prominent role of such leading technology companies as Facebook, Apple, Amazon, Netflix, and Google in applying AI to their businesses, but it goes beyond the FAANG cohort to look at AI applications in many nontechnology companies, including DHL and Fidelity. The chapters address such topics as retraining workers (who may be more ready for change than their companies are); the importance of motivated and knowledgeable leaders; the danger that AI will entrench less-than-ideal legacy processes; ways that AI could promote gender equality and diversity; AI and the global loneliness epidemic; and the benefits of robot-human collaboration. Contributors Cynthia M. Beath, Megan Beck, Joe Biron, Erik Brynjolfsson, Jacques Bughin, Rumman Chowdhury, Paul R. Daugherty, Thomas H. Davenport, Chris DeBrusk, Berkeley J. Dietvorst, Janet Foutty, James R. Freeland, R. Edward Freeman, Julian Friedland, Lynda Gratton, Francis Hintermann, Vivek Katyal, David Kiron, Frieda Klotz, Jonathan Lang, Barry Libert, Paul Michelman, Daniel Rock, Sam Ransbotham, Jeanne W. Ross, Eva Sage-Gavin, Chad Syverson, Monideepa Tarafdar, Gregory Unruh, Madhu Vazirani, H. James Wilson
Artificial Intelligence: ethics and regulation
The fourth age: smart robots, conscious computers, and the future of humanity by
The Fourth Age provides extraordinary background and context on how we got to this point, and how-- rather than what--we should think about the complex web of topics we'll soon all be facing: machine consciousness, automation, drastic shifts in employment and the workforce, creative computers, radical life extension, artificial life, the ethics of AI, autonomous warfare, superintelligence, and extreme prosperity, to name only a few. By asking questions like "Are you a machine?" and "Could a computer feel anything?" Reese leads the reader through a fascinating discussion along the cutting edge of robotics and AI. He provides a framework in which we can all understand, discuss, and act on the issues of the Fourth Age, and grasp how they will transform humanity.
Artificial Intelligence: Application
Intelligent automation welcome to the world of hyperautomation : learn how to harness artificial intelligence to boost business & make our world more human by 
Blockchain, Artificial Intelligence and Financial Services : Implications and Applications for Finance and Accounting Professionals by
Presenting a blend of technical analysis with current and future applications, this book provides professionals with an action plan to embrace and move forward with these new technologies in financial and accounting organizations.
Artificial Intelligence for Learning by
Artificial intelligence is creating huge opportunities for workplace learning and employee development. However, it can be difficult for L&D professionals to assess what difference AI can make in their organization and where it is best implemented. Artificial Intelligence for Learning is the practical guide L&D practitioners need to understand what AI is and how to use it to improve all aspects of learning in the workplace. It includes specific guidance on how AI can provide content curation and personalization to improve learner engagement, how it can be implemented to improve the efficiency of evaluation, assessment and reporting and how chatbots can provide learner support to a global workforce. Artificial Intelligence for Learning debunks the myths and cuts through the hype around AI allowing L&D practitioners to feel confident in their ability to critically assess where artificial intelligence can make a measurable difference and where it is worth investing in. There is also critical discussion of how AI is an aid to learning and development, not a replacement as well as how it can be used to boost the effectiveness of workplace learning, reduce drop off rates in online learning and improve ROI. With real-world examples from companies who have effectively implemented AI and seen the benefits as well as case studies from organizations including Netflix, British Airways and the NHS, this book is essential reading for all L&D practitioners needing to understand AI and what it means in practice.
Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches by
Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning
Artificial Intelligence in Practice by
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.
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