Applied Artificial Intelligence

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.

With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook Description You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You’ll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models. What you will learnCreate your first AI game in Python with the minmax algorithmImplement regression techniques to simplify real-world dataExperiment with classification techniques to label real-world dataPerform predictive analysis in Python using decision trees and random forestsUse clustering algorithms to group data without manual supportLearn how to use neural networks to process and classify labeled imagesWho this book is for The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

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.

"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 constitutes the refereed proceedings of the 19th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2006, held in Annecy, France, June 2006. The book presents 134 revised full papers together with 3 invited contributions, organized in topical sections on multi-agent systems, decision-support, genetic algorithms, data-mining and knowledge discovery, fuzzy logic, knowledge engineering, machine learning, speech recognition, systems for real life applications, and more.

"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

"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