This bestselling book gives business leaders and executives a foundational education on how to leverage artificial intelligence and machine learning solutions to deliver ROI for your business.

This book deals with artificial intelligence (AI) and its several applications. It is not an organic text that should be read from the first page onwards, but rather a collection of articles that can be read at will (or at need). The idea of this work is indeed to provide some food for thoughts on how AI is impacting few verticals (insurance and financial services), affecting horizontal and technical applications (speech recognition and blockchain), and changing organizational structures (introducing new figures or dealing with ethical issues). The structure of the chapter is very similar, so I hope the reader won’t find difficulties in establishing comparisons or understanding the differences between specific problems AI is being used for. The first chapter of the book is indeed showing the potential and the achievements of new AI techniques in the speech recognition domain, touching upon the topics of bots and conversational interfaces. The second and thirds chapter tackle instead verticals that are historically data-intensive but not data-driven, i.e., the financial sector and the insurance one. The following part of the book is the more technical one (and probably the most innovative), because looks at AI and its intersection with another exponential technology, namely the blockchain. Finally, the last chapters are instead more operative, because they concern new figures to be hired regardless of the organization or the sector, and ethical and moral issues related to the creation and implementation of new type of algorithms.

"This book is devoted mainly to applied expert systems. It does cover four additional applied AI Topics: natural language processing, computer vision, speech understanding and intelligent robotics"--Preface

"This book explores artificial intelligence finding it cannot simply display the high-level behaviours of an expert but must exhibit some of the low level behaviours common to human existence"--Provided by publisher.

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:• classification using standard machinery (naive bayes; nearest neighbor; SVM)• clustering and vector quantization (largely as in PSCS)• PCA (largely as in PSCS)• variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)• linear regression (largely as in PSCS)• generalized linear models including logistic regression• model selection with Lasso, elasticnet• robustness and m-estimators• Markov chains and HMM’s (largely as in PSCS)• EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they’ve been through that, the next one is easy• simple graphical models (in the variational inference section)• classification with neural networks, with a particular emphasis onimage classification• autoencoding with neural networks• structure learning

"Buy the paperback version of this book and get the kindle book version for free"Do you want to learn about new technologies to bring your business into the business of the 21st century? Artificial intelligence is proceeding forward to become the predominant element in human lives, whether businesses like it or not. Multinational companies have been able to leverage machine learning to gain insights into customer behavior and the intricacies of their own businesses to stay competitive and carry their businesses into the future. Standing toe-to-toe with these large companies may seem impossible with their huge data and staff resources, but artificial intelligence poises business leaders to level the playing field. Applied Artificial Intelligence teaches business leaders and data scientists how they can use intelligent technology to solve their business problems, scale AI technology to their business, use AI technology to train staff and develop leadership qualities, and keep up on the latest trends in IoT and business intelligence. The days of simply peddling a product and expecting a return are passed. The world of the modern day is characterized by information exchange, and this information exists in the form of data that need to be curated and analyzed. Businesses use data not only to make their businesses more competitive but literally to stay alive. Multinational businesses like Microsoft, Google, and Amazon are not giants in their industries because they developed sophisticated technologies and then stopped. These companies use data to stay competitive, and smaller companies will have to do the same if they expect to survive. Applied artificial intelligence refers to leveraging intelligent technology to increase the productivity of a business. This term acknowledges that artificial intelligence can be something abstract that computer scientists and data scientists experiment with in order to get a glimpse of our collective technological future, but it can also be a technology that has real practical aspects to businesses. The purpose of this book is to help the reader approach artificial intelligence pragmatically. The reader will be provided with strategies that other businesses use to integrate their data with smart technology. They will also be informed of the latest trends in business intelligence as well as given numerous examples of the many ways that businesses of every type are using machine learning to leverage profits. The idea that artificial intelligence is something scary that big, multinational companies use to do mysterious things with data is a viewpoint that is not helpful to any business owner. Artificial intelligence is shaping the future, and it is up to business leaders to recognize this and stay abreast of the latest trends and strategies. Would you like to know more?Scroll to the top of the page and select the buy now button.

This book offers students an introduction to the concepts of big data and artificial intelligence (AI) and their applications in the business world. It answers questions such as what are the main concepts of artificial intelligence and big data? What applications for artificial intelligence and big data analytics are used in the business field? It offers application-oriented overviews and cases from different sectors and fields to help readers discover and gain useful insights. Each chapter features discussion questions and summaries. To assist professors in teaching, the book supplementary materials will include answers to questions, and presentation slides.

This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.

Why yet another book on Artificial Intelligence? It is true that hundreds of publications on Artificial Intelligence (AI) have been published within the last decades - scientific papers and text books. Most of them focus on the theory behind AI solutions: logic, reasoning, statistical foundations, etc. However, little can be found on engineering AI applications.Modern, complex IT applications are not built from scratch but by integrating off-the-shelf components: libraries, frameworks, and services. The same applies, of course, for AI applications. Over the last decades, numerous off-the-shelf components for AI base functionality such as logic, reasoning, and statistics have been implemented - commercial and open source. Integrating such components into user friendly, high-performance, and maintainable AI applications requires specific engineering skills. "Applied Artificial Intelligence - An Engingeering Approach" focuses on those skills.