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Book SynopsisDino Esposito: If I look back, I count more 20 books authored and 1000+ articles in a 25-year-long career. I've been writing the Cutting Edge column for MSDN Magazine month after month for 22 consecutive years. It is commonly recognized that such books and articles have helped the professional growth of thousands of .NET and ASP.NET developers and software architects worldwide. After I escaped a dreadful COBOL project, in 1992 I started as a C developer, and since then, I have witnessed MFC and ATL, COM and DCOM, the debut of .NET, the rise and fall of Silverlight, and the ups and downs of various architectural patterns. In 1995 I led a team of five dreamers who actually deployed things that today we would call Google Photos and Shuttershockdesktop applications capable of dealing with photos stored in a virtual place that nobody had called the cloud yet. Since 2003 I have written Microsoft Press books about ASP.NET and also authored the bestseller MicrosoTable of Contents Introduction Part I Laying the Groundwork of Machine Learning Chapter 1 How Humans Learn The Journey Toward Thinking Machines The Dawn of Mechanical Reasoning Godel’s Incompleteness Theorems Formalization of Computing Machines Toward the Formalization of Human Thought The Birth of Artificial Intelligence as a Discipline The Biology of Learning What Is Intelligent Software, Anyway? How Neurons Work The Carrot-and-Stick Approach Adaptability to Changes Artificial Forms of Intelligence Primordial Intelligence Expert Systems Autonomous Systems Artificial Forms of Sentiment Summary Chapter 2 Intelligent Software Applied Artificial Intelligence Evolution of Software Intelligence Expert Systems General Artificial Intelligence Unsupervised Learning Supervised Learning Summary Chapter 3 Mapping Problems and Algorithms Fundamental Problems Classifying Objects Predicting Results Grouping Objects More Complex Problems Image Classification Object Detection Text Analytics Automated Machine Learning Aspects of an AutoML Platform The AutoML Model Builder in Action Summary Chapter 4 General Steps for a Machine Learning Solution Data Collection Data-Driven Culture in the Organization Storage Options Data Preparation Improving Data Quality Cleaning Data Feature Engineering Finalizing the Training Dataset Model Selection and Training The Algorithm Cheat Sheet The Case for Neural Networks Evaluation of the Model Performance Deployment of the Model Choosing the Appropriate Hosting Platform Exposing an API Summary Chapter 5 The Data Factor Data Quality Data Validity Data Collection Data Integrity Completeness Uniqueness Timeliness Accuracy Consistency What’s a Data Scientist, Anyway? The Data Scientist at Work The Data Scientist Tool Chest Data Scientists and Software Developers Summary Part II Machine Learning In .NET Chapter 6 The .NET Way Why (Not) Python? Why Is Python So Popular in Machine Learning? Taxonomy of Python Machine Learning Libraries End-to-End Solutions on Top of Python Models Introducing ML.NET Creating and Consuming Models in ML.NET Elements of the Learning Context Summary Chapter 7 Implementing the ML.NET Pipeline The Data to Start From Exploring the Dataset Applying Common Data Transformations Considerations on the Dataset The Training Step Picking an Algorithm Measuring the Actual Value of an Algorithm Planning the Testing Phase A Look at the Metrics Price Prediction from Within a Client Application Getting the Model File Setting Up the ASP.NET Application Making a Taxi Fare Prediction Devising an Adequate User Interface Questioning Data and Approach to the Problem Summary Chapter 8 ML.NET Tasks and Algorithms The Overall ML.NET Architecture Involved Types and Interfaces Data Representation Supported Catalogs Classification Tasks Binary Classification Multiclass Classification Clustering Tasks Preparing Data for Work Training the Model Evaluating the Model Transfer Learning Steps for Building an Image Classifier Applying Necessary Data Transformations Composing and Training the Model Margin Notes on Transfer Learning Summary Part III Fundamentals of Shallow Learning Chapter 9 Math Foundations of Machine Learning Under the Umbrella of Statistics The Mean in Statistics The Mode in Statistics The Median in Statistics Bias and Variance The Variance in Statistics The Bias in Statistics Data Representation Five-number Summary Histograms Scatter Plots Scatter Plot Matrices Plotting at the Appropriate Scale Summary Chapter 10 Metrics of Machine Learning Statistics vs. Machine Learning The Ultimate Goal of Machine Learning From Statistical Models to Machine Learning Models Evaluation of a Machine Learning Model From Dataset to Predictions Measuring the Precision of a Model Preparing Data for Processing Scaling Standardization Normalization Summary Chapter 11 How to Make Simple Predictions: Linear Regression The Problem Guessing Results Guided by Data Making Hypotheses About the Relationship The Linear Algorithm The General Idea Identifying the Cost Function The Ordinary Least Square Algorithm The Gradient Descent Algorithm How Good Is the Algorithm? Improving the Solution The Polynomial Route Regularization Summary Chapter 12 How to Make Complex Predictions and Decisions: Trees The Problem What’s a Tree, Anyway? Trees in Machine Learning A Sample Tree-Based Algorithm Design Principles for Tree-Based Algorithms Decision Trees versus Expert Systems Flavors of Tree Algorithms Classification Trees How the CART Algorithm Works How the ID3 Algorithm Works Regression Trees How the Algorithm Works Tree Pruning Summary Chapter 13 How to Make Better Decisions: Ensemble Methods The Problem The Bagging Technique Random Forest Algorithms Steps of the Algorithms Pros and Cons The Boosting Technique The Power of Boosting Gradient Boosting Pros and Cons Summary Chapter 14 Probabilistic Methods: Naïve Bayes Quick Introduction to Bayesian Statistics Introducing Bayesian Probability Some Preliminary Notation Bayes’ Theorem A Practical Code Review Example Applying Bayesian Statistics to Classification Initial Formulation of the Problem A Simplified (Yet Effective) Formulation Practical Aspects of Bayesian Classifiers Naïve Bayes Classifiers The General Algorithm Multinomial Naïve Bayes Bernoulli Naïve Bayes Gaussian Naïve Bayes Naïve Bayes Regression Foundation of Bayesian Linear Regression Applications of Bayesian Linear Regression Summary Chapter 15 How to Group Data: Classification and Clustering A Basic Approach to Supervised Classification The K-Nearest Neighbors Algorithm Steps of the Algorithm Business Scenarios Support Vector Machine Overview of the Algorithm A Quick Mathematical Refresher Steps of the Algorithm Unsupervised Clustering A Business Case: Reducing the Dataset The K-Means Algorithm The K-Modes Algorithm The DBSCAN Algorithm Summary Part IV Fundamentals of Deep Learning Chapter 16 Feed-Forward Neural Networks A Brief History of Neural Networks The McCulloch-Pitt Neuron Feed-Forward Networks More Sophisticated Networks Types of Artificial Neurons The Perceptron Neuron The Logistic Neuron Training a Neural Network The Overall Learning Strategy The Backpropagation Algorithm Summary Chapter 17 Design of a Neural Network Aspects of a Neural Network Activation Functions Hidden Layers The Output Layer Building a Neural Network Available Frameworks Your First Neural Network in Keras Neural Networks versus Other Algorithms Summary Chapter 18 Other Types of Neural Networks Common Issues of Feed-Forward Neural Networks Recurrent Neural Networks Anatomy of a Stateful Neural Network LSTM Neural Networks Convolutional Neural Networks Image Classification and Recognition The Convolutional Layer The Pooling Layer The Fully Connected Layer Further Neural Network Developments Generative Adversarial Neural Networks Auto-Encoders Summary Chapter 19 Sentiment Analysis: An End-to-End Solution Preparing Data for Training Formalizing the Problem Getting the Data. Manipulating the Data Considerations on the Intermediate Format Training the Model Choosing the Ecosystem Building a Dictionary of Words Choosing the Trainer Other Aspects of the Network The Client Application Getting Input for the Model Getting the Prediction from the Model Turning the Response into Usable Information Summary Part V Final Thoughts Chapter 20 AI Cloud Services for the Real World Azure Cognitive Services Azure Machine Learning Studio Azure Machine Learning Service Data Science Virtual Machines On-Premises Services SQL Server Machine Learning Services Machine Learning Server Microsoft Data Processing Services Azure Data Lake Azure Databricks Azure HDInsight .NET for Apache Spark Azure Data Share Azure Data Factory Summary Chapter 21 The Business Perception of AI Perception of AI in the Industry Realizing the Potential What Artificial Intelligence Can Do for You Challenges Around the Corner End-to-End Solutions Let’s Just Call It Consulting The Borderline Between Software and Data Science Agile AI Summary 9780135565667 TOC 12/19/2019

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Book SynopsisArti?cial 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 usiTable of Contents1. Introduction to AI & ML. 2. Impact of AI & ML in Operations Management. 3. Impact of AI & ML in Marketing. 4. Impact of AI & ML in Human Resource Management. 5. Impact of AI & ML in Finance. 6. Impact of AI & ML in Strategy. 7. Impact of AI & ML on Society and Governments. 8. Logistics and Supply Chain Management. 9. Manufacturing and Industrial. 10. Advertisement and Customer Relationship. 11. Knowledge Management, Talent Management. 12. E-Governance and E-Learning.

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Book SynopsisMachine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to m

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Book SynopsisMichael Miller has written more than 200 books over the past four decades, including many in the Absolute Beginner's Guide series and almost two dozen books with AARP. His books have collectively sold more than 1.5 million copies worldwide. He has a particular interest in new and evolving technologies, so writing about AI is right up his alley. His readers say he has a knack for explaining complex technologies in easy-to-understand terms, and he has no reason to argue with that. He is an older gentleman, which means he's been around long enough to experience many different new technologies. (He has been writing for four decades; he wrote one of the first books about the Internet when it was new!) He lives with his wife and random daily combinations of four stepchildren and eight grandchildren in a suburb of the Twin Cities in the often-frozen land of Minnesota. In his spare time, he plays drums, entertains (and is entertained by) his grandchildren, and

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Book SynopsisThis book introduces foundational and advanced concepts in artificial intelligence (AI) and machine learning (ML), focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and generative AI, Artificial Intelligence and Machine Learning for Real-World Applications: A Beginner's Guide with Case Studies includes practical Python implementations and case studies from healthcare, agriculture, and education. Beginning with core concepts such as AI fundamentals, knowledge representation, and statistical techniques, the text gradually advances to cover ML algorithms, deep learning architectures, and the basics of generative AI. Detailed discussions of data preprocessing, model training, evaluation metrics, and Python-based implementation make this book both practical and accessible. Offers real-world examples and case studies illustrating the societal impact and practical applications of AI and ML technologies Discusses data preprocessing techniques, model selection, and evaluation metrics with practical implementation in Python and in detail Explores AI problem-solving processes, knowledge representation, and model training strategies, catering to readers with varying levels of technical expertise Covers AI and ML principles spanning statistical techniques, ML algorithms, deep learning structures, and generative AI basics Focuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by the elderly and special needs individuals This book is for professionals, researchers, and scholars interested in the application of AI and ML.

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Book SynopsisThis book introduces foundational and advanced concepts in artificial intelligence (AI) and machine learning (ML), focusing on their real-world applications and societal implications. Covering topics from knowledge representation and model interpretability to deep learning and generative AI, Artificial Intelligence and Machine Learning for Real-World Applications: A Beginner's Guide with Case Studies includes practical Python implementations and case studies from healthcare, agriculture, and education. Beginning with core concepts such as AI fundamentals, knowledge representation, and statistical techniques, the text gradually advances to cover ML algorithms, deep learning architectures, and the basics of generative AI. Detailed discussions of data preprocessing, model training, evaluation metrics, and Python-based implementation make this book both practical and accessible. Offers real-world examples and case studies illustrating the societal impact and practical applications of AI and ML technologies Discusses data preprocessing techniques, model selection, and evaluation metrics with practical implementation in Python and in detail Explores AI problem-solving processes, knowledge representation, and model training strategies, catering to readers with varying levels of technical expertise Covers AI and ML principles spanning statistical techniques, ML algorithms, deep learning structures, and generative AI basics Focuses on societal applications in healthcare, agriculture, and education, addressing challenges faced by the elderly and special needs individuals This book is for professionals, researchers, and scholars interested in the application of AI and ML.

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Book SynopsisThe significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stockTable of ContentsForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis

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Book SynopsisThe book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field.It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models.This is a core resource for students and instructors of machine learning and data science looking for beginner-

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Book Synopsis'An invaluable companion for anyone who wants a deep understanding of what's under the hood of often inscrutable machines' Melanie Mitchell A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligenceMachine-learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumour is cancerous, or deciding whether someone gets bail. They now influence discoveries in chemistry, biology and physics - the study of genomes, extra-solar planets, even the intricacies of quantum systems. We are living through a revolution in artificial intelligence that is not slowing down. This major shift is based on simple mathematics, some of which goes back centuries: linear algebra and calculus, the stuff of eighteenth-century mathematics. Indeed by the mid-1850s, a lot of the groundwork was all done. It took the development of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today. In this enlightening book, Anil Ananthaswamy explains the fundamental maths behind AI, which suggests that the basics of natural and artificial intelligence might follow the same mathematical rules. As Ananthaswamy resonantly concludes, to make the most of our most wondrous technologies we need to understand their profound limitations - the clues lie in the maths that makes AI possible.

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Book SynopsisThis book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.Trade Review'An authoritative treatment of modern machine learning, covering a broad range of topics, for readers who want to use and understand machine learning.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems'This book provides the perfect introduction to modern machine learning, with an ideal balance between mathematical depth and breadth. Its outstanding clarity and many illustrations make it a perfect tool for self-learning or as a textbook for an introductory machine learning class.' Francis Bach, Inria - Ecole Normale Supérieure'Lucid and engaging, this book is a brilliant companion to anyone with a numerate background who wants to know what really goes on under the hood in supervised learning. The core theory and rich illustrative examples enable practitioners navigate the jungle of modern machine learning.' Carl Edward Rasmussen, University of Cambridge'This book provides an excellent introduction to machine learning for engineers and scientists. It covers the main techniques in this exciting area ranging from basic approaches, such as linear regression and principal component analysis, to modern deep learning and generative modelling techniques. The authors have managed to find the right balance between academic rigor, intuition and applications. Required reading for any newcomer interested in this field!' Arnaud Doucet, University of Oxford'This book strikes a very good balance between accessibility and rigour. It will be a very good companion for the mathematically trained who want to understand the hows and whats of machine learning.' Ole Winther, University of Copenhagen and Technical University of DenmarkTable of Contents1. Introduction; 2. Supervised learning: a first approach; 3. Basic parametric models and a statistical perspective on learning; 4. Understanding, evaluating and improving the performance; 5. Learning parametric models; 6. Neural networks and deep learning; 7. Ensemble methods: Bagging and boosting; 8. Nonlinear input transformations and kernels; 9. The Bayesian approach and Gaussian processes; 10. Generative models and learning from unlabeled data; 11. User aspects of machine learning; 12. Ethics in machine learning.

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Book SynopsisThe book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.In summary, this book provides a comprehensive technological path from fundamentalTable of Contents1. Introduction 2. Linear Algebra 3. Machine Learning 4. Some Practical Notes 5. Deep Learning 6. Generative Adversarial Networks 7. Implementation

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Book SynopsisA First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.âDevdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, SwedenThis textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.âDaniel Barbara, George Mason University, Fairfax, Virginia, USAThe new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introductioTrade Review"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process—many other texts omit such details, leaving them as ‘an exercise for the reader.’ Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK"In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!"—Aleksandar Ignjatovic, University of New South Wales"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introductTable of ContentsLinear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.

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Book Synopsis With the idea of “deep learning” having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing. Table of ContentsList of Figures ix Preface xiii Introduction xxi Chapter 1. What is Intelligence? 1 1.1. Intelligence 1 1.2. Business Intelligence 2 1.3. Artificial Intelligence 5 1.4. How BI has developed 6 1.4.1. BI 1.0 7 1.4.2. BI 2.0 8 1.4.3. And beyond 11 Chapter 2. Digital Learning 13 2.1. What is learning? 13 2.2. Digital learning 14 2.3. The Internet has changed the game 16 2.4. Big Data and the Internet of Things will reshuffle the cards 18 2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning 21 2.6. Supervised learning 22 2.7. Enhanced supervised learning 24 2.8. Unsupervised learning 28 Chapter 3. The Reign of Algorithms 33 3.1. What is an algorithm? 34 3.2. A brief history of AI 34 3.2.1. Between the 1940s and 1950s 35 3.2.2. Beginning of the 1960s 36 3.2.3. The 1970s 37 3.2.4. The 1980s 37 3.2.5. The 1990s 38 3.2.6. The 2000s 38 3.3. Algorithms are based on neural networks, but what does this mean? 39 3.4. Why do Big Data and AI work so well together? 42 Chapter 4. Uses for Artificial Intelligence 47 4.1. Customer experience management 48 4.1.1. What role have smartphones and tablets played in this relationship? 50 4.1.2. CXM is more than just a software package 51 4.1.3. Components of CXM 53 4.2. The transport industry 55 4.3. The medical industry 58 4.4. “Smart” personal assistant (or agent) 60 4.5. Image and sound recognition 62 4.6. Recommendation tools 65 4.6.1. Collaborative filtering (a “collaborative” recommendation mode) 66 Conclusion 71 Appendices 75 Appendix 1. Big Data 77 Appendix 2. Smart Data 83 Appendix 3. Data Lakes 89 Appendix 4. Some Vocabulary Relevant to 93 Appendix 5. Comparison Between Machine Learning and Traditional Business Intelligence 101 Appendix 6. Conceptual Outline of the Steps Required to Implement a Customization Solution based on Machine Learning 103 Bibliography 107 Glossary 111 Index 115

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Book SynopsisLos modelos de inteligencia artificial generativa (IAG) y de lenguajes de inteligencia artificial son cada vez más conocidos por sus incomparables capacidades. Este volumen ofrece información sobre el funcionamiento interno de los LLM o grandes modelos de lenguaje y una guía para la creación de modelos de lenguaje propios. Comienza con una introducción al campo de la IAG que le permitirá comprender cómo se entrenan estos modelos para generar nuevos datos. Tendrá además la oportunidad de explorar casos prácticos en los que ChatGPT ha logrado mejorar la productividad y fomentar la creatividad. Aprenderá cómo sacar el máximo partido de sus interacciones con ChatGPT enriqueciendo el diseño de prompts y aprovechando las capacidades de aprendizaje zero-shot, one-shot y few-shot, es decir, con cero, uno y pocos ejemplos por clase. Los casos prácticos están agrupados por los ámbitos de técnicos de marketing, investigadores y desarrolladores o científicos de datos, lo que le permitirá ap

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Book SynopsisDominar las técnicas de modelado y la comprensión de los datos, verdadero combustible de la inteligencia artificial, se han convertido en habilidades clave en una sociedad transformada por la revolución digital. Tanto si está empezando como si busca nuevas habilidades y competencias, este libro le guía a través del mundo de la ciencia de datos, una disciplina que trasciende los límites de la programación para extraer información relevante y diseñar sistemas que puedan ofrecer soluciones concretas en todas las áreas. Diseñado como un verdadero manual de usuario, este libro le acompaña a través de todas las etapas del procesamiento y análisis de datos: recopilación, preparación, exploración, modelado predictivo y aplicación. Con Python y sus librerías imprescindibles, descubrirá un método claro y numerosos ejemplos concretos para convertir sus datos en conocimiento y valor añadido, de tal manera que pueda aplicar inmediatamente los conceptos aprendidos. Usted aprendera a: - entender los mecanismos principales de la ciencia de datos, - importar, manipular y visualizar datos complejos con herramientas como Pandas, Seaborn y Matplotlib, - analizar las variables con Scipy y Statmodels, - aplicar algoritmos de Machine Learning para resolver problemas reales sobre sus datos, imágenes o texto, - automatizar y estructurar sus análisis en un entorno rico y accesible.

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Book SynopsisIn alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.

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Book SynopsisThis book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic.A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice.As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.Table of ContentsPART I: INTRODUCTION Editorial: Benefits and challenges of AI/ML in hybrid imaging and molecular imaging P. Veit-Haibach and Ken Herrmann PART II: TECHNOLOGY Role and influence of AI/ML in healthcare and specifically hybrid imaging and molecular imaging (Benjamin L. Franc and Guido Davidzon) Radiomics, Radiogenomics, AI, Deep-Learning and Machine Learning - Definitions and Imaging Applications (Jens Kleesiek, Germany) Radiomics in Nuclear Medicine - Robustness, Reproducibility, Standardisation (Reza Rezai, Toronto) Evolution of AI/ML in molecular imaging – we did this before AI based applications in Hybrid Imaging – how to build smart and truly multi-parametric decision models Basic principles of neural networks: types, applications and “usefulness” for molecular imaging (Josh Kaggie, Cambridge, UK, Chitresh Bhushan, GE, Niskayuna, NY, US and Dawei Gui, GE, Waukesha, Wi, US) PART III CLINICAL APPLICATIONS Imaging biomarkers and their meaning for molecular imaging (Angel Alberich Bayarri, Valencia, Spain) Integration of AI/ML in Clinical Routine in Molecular Imaging Structured reporting with AI/ML in Molecular Imaging in Theranostics Imaging biobanks for molecular imaging – how to integrate ML/AI into our databases Angel Alberich Bayarri, Valencia, Spain) AI/ML Imaging applications in head and neck diseases (neuro/onco) (Mijin Jun, Seoul, Korea for neurodegenerative applications) AI/ML Imaging applications in body oncology (Robert Seifert et al, Muenster, Germany) AI/ML imaging applications in cardiac imaging (Piotr Slomka, LA, US) AI/ML Imaging applications in therapy follow up and therapy decision support PART IV: Impact of A.I. and ML on Molecular Imaging and Theranostics AI/ML will help to improve Molecular Imaging as well as the Molecular Therapy/Theranostic – what are the biggest advantages for Imaging and Therapy?. (Benjamin L. Franc and ?) Why imaging data is not enough – AI based integration of imaging and clinical data Legal and ethical issues in AI/ML – why does the patient does not own his /her data? (Prainsack, Vienna) The role of A.I./ML for clinical trials in molecular imaging Show me the money – investor landscape in AI/ML in molecular imaging and therapy Physician centered imaging interpretation is dying out - why should I be a radiologist/nuclear medicine physician or how do we attract the smartest people to our field (Roland Hustinx, Liege, Belgium) Advantages and risks of A.I./ML for Molecular Imaging Specialists – what do we need to learn to master the “beast”

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Book SynopsisThis book investigates the transformative impact of AI, ML, Data Analytics, and IoT on modern business, emphasising their roles in strategic growth, operational efficiency, and data-driven decision-making. Through real-world case studies, strategic frameworks, and ethical considerations, it offers actionable insights for integrating these technologies. Covering emerging trends and responsible adoption, this book is an essential resource for professionals navigating digital transformation and achieving sustainable success. Explore the application of AI, ML, Data Analytics, and IoT within business contexts, emphasising their roles in strategic growth, operational efficiency, and decision-making. Provides methodologies for maximising ROI, mitigating risks, and fostering innovation. Explores predictive modeling, market segmentation, and business intelligence for competitive advantage. Examines cutting-edge automation technologies that optimise processes and enhance workforce efficiency. Investigates neuro-fuzzy systems in financial forecasting to improve predictive accuracy and decision-making. This book serves as a practical guide for professionals and a reference for scholars, researchers and students of computer science and business management

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Book SynopsisA First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.âDevdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, SwedenThis textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.âDaniel Barbara, George Mason University, Fairfax, Virginia, USAThe new edition of A First Course in Machine Learning by Rogers and Girolami is an excTrade Review"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process—many other texts omit such details, leaving them as ‘an exercise for the reader.’ Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK"In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!"—Aleksandar Ignjatovic, University of New South Wales"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introductTable of ContentsLinear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.

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Book SynopsisThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely from scratch based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.Trade Review'Dr Jiang has done a superb job in covering many methods, both theoretical and practical, across a broad spectrum of machine learning in this timely book. I worked closely with Dr Jiang on Bayesian speech recognition during late 90's and I have personally witnessed his excellent skills in applying machine learning to solving a wide range of practical problems. In this book, Dr Jiang has expanded his scope into a much wider set of logically organized topics in modern machine learning. The organization of the material is highly unique and cogent. A number of hot topics in machine learning, including deep learning and neural networks, are naturally incorporated in the book, which not only provides sufficient technical depth for the readers but also aligns well with popular toolkits for implementing the related machine learning methods.' Li Deng, formerly of Microsoft Corporation and Citadel LLC'It is beautifully designed, with many color images that make the complex subject matter manageable … It is a book for students and developers who are committed to specializing in ML or a specific area of ​​it.' Karl van Heijster , De Leesclub van AllesTable of Contents1. Introduction; 2. Mathematical Foundation; 3. Supervised Machine Learning (in a nutshell); 4. Feature Extraction; 5. Statistical Learning Theory; 6. Linear Models; 7. Learning Discriminative Models in General; 8. Neural Networks; 9. Ensemble Learning; 10. Overview of Generative Models; 11. Unimodal Models; 12. Mixture Models; 13. Entangled Models; 14. Bayesian Learning; 15. Graphical Models.

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Book SynopsisMachine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML.The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects ComprehensiveTable of Contents 1. Introduction to Signal Processing and Machine Learning Kavitha Somaraj 2. Learning Theory (Supervised/Unsupervised) for Signal Processing Ruby Jain, Bhuvan Jain, and Manimala Puri 3. Supervised and Unsupervised Learning Theory for Signal Processing Sowmya K. B. 4. Applications of Signal Processing Anuj Kumar Singh and Ankit Garg 5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing V. Ajantha Devi and Mohd Naved 6. Brain–Computer Interfacing Paras Nath Singh 7. Adaptive Filters and Neural Net Sowmya K. B., Chandana G., and Anjana Mahaveer Daigond 8. Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network Saikat Majumder 9. Intelligent Video Surveillance Systems Using Deep Learning Methods Anjanadevi Bondalapati and Manjaiah D. H. 10. Stationary Signal, Autocorrelation, and Linear and Discriminant Analysis Bandana Mahapatra and Kumar Sanjay Bhorekar 11. Intelligent System for Fault Detection in Rotating Electromechanical Machines. Pascal Dore, Saad Chakkor, and Ahmed El Oualkadi 12. Wavelet Transformation and Machine Learning Techniques for Digital Signal Analysis in IoT Systems Rajalakshmi Krishnamurthi and Dhanalekshmi Gopinathan

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Book SynopsisoneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing the complexity as well as the cost and time of software development. One of the significant strengths of oneAPI is in its capability to support an eclectic range of devices and architectures, including artificial intelligence, high-performance computing, and data analytics. Along with libraries, tools, and compilers, oneAPI makes it cool for developers to create optimized code for an extensive variety of applications, making it an indispensable tool for any developer who wants to create high-performance software and reap the benefit of the latest hardware technologies. The versatility of oneAPI, by means of appropriate theory and practical implementatioTable of ContentsChapter 1 Intel oneAPI: An Introductory DiscussionChapter 2 The Intel oneAPI Toolkits: An Exploration Chapter 3 The Intel DevCloud and Jupyter Notebooks Chapter 4 What Is Machine Learning?: An Introduction! Chapter 5 Tools and Pre-requisites Chapter 6 Supervised Learning Chapter 7 Support Vector Machines (SVM): An Exploration Chapter 8 Decision Trees Chapter 9 Bagging Chapter 10 Boosting and Stacking Chapter 11 Clustering Techniques and Principial Component Analysis Chapter 12 More Intel Tools for Enhanced Developm ent Experience

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Book SynopsisPredictive Analytics.- Machine Learning .- Artificial Intelligence .- Data Mining.- Clinical Risk Models.- Clinical Risk Stratification.- Data Science.- Causal Discovery.- Causal Inference.- Causal Discovery in Health Sciences.- Causal Inference In Health Sciences.- Ehr Data Analytics.- Medical Knowledge Discovery.- Biomedical Machine Learning.- Biomedical Artificial Intelligence.- Healthcare Machine Learning.- Healthcare Artificial Intelligence.- Translational Science Machine Learning.- Machine Learning for Biological Discovery.- Machine Learning in Bioinformatics.- Machine Learning in Genomics.

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Book SynopsisImage processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image prTable of ContentsPreface Volume 1. 1. Pixel Operations. 2. Spatial Filtering. 3. Edge Detection. 4. Segmentation and Processing of Binary Images. 5. Corner Detection. 6. Line Detection. Index.

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Book SynopsisMachine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation ComparisonTable of Contents 1. Introduction. 2. Understanding Machine Learning. 3. Mathematiccal Foundations and Machine Learning. 4. Foundations and categoris of Machine Learning Techniques. 5. Machine Learning: Tool and Software 6. Classification Algorithms. 7. Probabilistic and Regression based approaches. 8. Performance Evaluation & Ensemble Methods. 9. Unsupervised Learning. 10. Sequence Models. 11. Reinforcement Learning. 12. Machine Learning Applications – Approaches. 13. Domain based Machine Learning Applications. 14. Ethical Aspects of Machine Learning. 15. Introduction to Deep Learning and Convolutional Neural Networks. 16. Other Models of Deep Learning and Applications of Deep Learning.

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Book SynopsisThe book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target diffe

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Book SynopsisRoi Yehoshua is a professor in the Department of Electrical and Computer Engineering at Northeastern University, where he develops and teaches graduate courses in machine learning and data science. With over two decades of experience spanning academia and industry, he has developed and taught a wide range of machine learning courses, including pioneering the university's first course on Large Language Models. His writing on machine learning has reached over 200,000 readers worldwide through platforms like Medium and Towards Data Science.

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Book SynopsisArtificial Intelligence for Academic Libraries provides a clear and dependable guide to the history, theory, and application of artificial intelligence (AI) and machine learning (ML) in academic libraries, addressing the needs of librarians, staff, administrators, and other stakeholders. Emerging from a long-running conversation between an academic librarian and a professor of computer science, this book provides readers with a critical perspective on the history, present, and future of AI in libraries and information services. Synthesizing the literature on AI, ML, and librarianship, the authors provide the requisite background to evaluate the impact of these technologies on the information ecosystem. The first half of the volume covers the fundamentals of AI, notably the divergences between the two major AI paradigms as well as philosophical, legal, and ethical issues that arise from the use of AI. The second half addresses specialized topics, including hybrid AIs that bridge the dominant AI paradigms, the responsible use of AI in research and learning, and suggestions for professional development. Artificial Intelligence for Academic Libraries will benefit academic librarians seeking to develop a solid understanding of AI and ML and their likely impact on library operations and services, including student and faculty outreach. The volume also serves as a pedagogical resource for library and information science students entering this rapidly changing field.

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Book SynopsisMachine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.Trade Review'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, BerkeleyTable of Contents1. Introduction; Part I. Foundations: 2. A gentle start; 3. A formal learning model; 4. Learning via uniform convergence; 5. The bias-complexity trade-off; 6. The VC-dimension; 7. Non-uniform learnability; 8. The runtime of learning; Part II. From Theory to Algorithms: 9. Linear predictors; 10. Boosting; 11. Model selection and validation; 12. Convex learning problems; 13. Regularization and stability; 14. Stochastic gradient descent; 15. Support vector machines; 16. Kernel methods; 17. Multiclass, ranking, and complex prediction problems; 18. Decision trees; 19. Nearest neighbor; 20. Neural networks; Part III. Additional Learning Models: 21. Online learning; 22. Clustering; 23. Dimensionality reduction; 24. Generative models; 25. Feature selection and generation; Part IV. Advanced Theory: 26. Rademacher complexities; 27. Covering numbers; 28. Proof of the fundamental theorem of learning theory; 29. Multiclass learnability; 30. Compression bounds; 31. PAC-Bayes; Appendix A. Technical lemmas; Appendix B. Measure concentration; Appendix C. Linear algebra.

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Book SynopsisLa inteligencia artificial ha capturado titulares y transformado conversaciones en todo el mundo, pero su verdadera esencia y recorrido histórico a menudo se desconocen o se malinterpretan. En Historia de la Inteligencia Artificial, Sara Robisco, científica de datos especializada en deep learning, ofrece una exploración precisa y accesible de este revolucionario campo de la ciencia.Robisco desmitifica la IA al trazar su evolución desde los años 50 hasta nuestros días, revelando cómo muchas de las técnicas actuales, consideradas innovadoras, tienen profundas raíces en la historia de la informática. Desde los primeros sistemas expertos hasta las avanzadas redes neuronales, cada capítulo ilumina aspectos clave, haciendo hincapié en su impacto en el mundo actual.El libro profundiza en el manejo de grandes volúmenes de datos, explicando de manera sencilla cómo se gestionan y procesan en la era del big data. La autora resalta la importancia crítica de la calidad de los datos y revela cómo los sesgos presentes en ellos pueden distorsionar los resultados algorítmicos. También se exploran cuestiones éticas y de seguridad, temas que se vuelven cada vez más urgentes en el entorno digital.Con una combinación de rigor técnico y claridad expositiva, Robisco explica de forma comprensible algoritmos complejos como k-means, dbscan y redes neuronales convolucionales, acompañándolos de ejemplos prácticos. El lector descubrirá aplicaciones reales de la IA en campos tan variados como la medicina, las finanzas o el procesamiento del lenguaje natural.Este libro proporciona al lector las herramientas necesarias para interpretar con criterio las noticias sobre IA, diferenciando entre avances reales y exageraciones mediáticas. Tanto si eres un profesional del sector, un estudiante curioso o simplemente un entusiasta de la tecnología, este libro te brindará una comprensión profunda de la IA, su historia y su enorme potencial transformador.

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Book SynopsisMachine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.Trade Review"If you are considering implementing machine learning in your business but don’t know where to start, this is the right book for you. Machine Learning for Managers is a comprehensive but non-technical introduction to the topic with many relevant examples and implementation guidelines. The split into a detailed overview and project management instructions is ideal for readers who don’t have the time to acquire programming skills but are passionate about leveraging AI to enhance business performance. The author’s very engaging writing style makes reading a book about a potentially very dry topic enjoyable."Christoph Schumacher, Professor of Innovation and Economics; Director Knowledge Exchange Hub, Massey University, New Zealand"This book fills an important gap between pure-technical and pure-managerial descriptions of machine learning (ML). Written in a no-nonsense light-hearted style, it is easy to follow, yet doesn’t shy away from using technical terms that are important for managers to be able to speak to their ML engineers. Highly recommended for managers looking to understand more about what is under the hood of ML."Tava Olsen, Professor, Deputy Dean, Melbourne Business School"Machine Learning for Managers is a safe haven for non-technical readers interested in understanding what AI and specifically ML is about. With clear, direct and witty language, Geertsema ensures that our journey into AI is like a walk in the park. It is easy, pleasurable and refreshing in its approach and powerful in its choice of illustrations. It brings to the forefront key concepts such as explainability, governance and business case making the message lucent and highly applicable to managers interested in incorporating ML into their business. As a practitioner focussed on human centric AI, I am particularly keen in bringing down AI/ML from its ivory tower status. This book is exactly a tool for this as it provides transparency, deciphers otherwise perceived complex language and is the basis for what ML should do best: to serve you. By far the best introductory ML roadmap I have come across. A must read."Jose Romano, Senior Manager at the European Investment Fund and former Entrepreneur in Residence at TAZI.AI"The two complementary parts of the book form a comprehensive and practical guide to machine learning. The first part provides a nontechnical overview of machine learning algorithms, demystifying the jargon in the field, which is crucial for students, lecturers and practitioners aiming to apply machine learning to resolve real-life business problems. The second part insightfully examines how machine learning outcomes can be developed and deployed in the organisation's processes. A recommended work for anyone looking to successfully manage the tsunami of big data!"Leo Paas, Professor, The University of Auckland Business School; Program Director, Master of Business Analytics"This book provides an outstanding introduction to machine learning from a management perspective. It gives a very clear presentation of the state-of-the-art machine learning methods and how to manage machine learning projects efficiently. It brings a fresh, unique focus on how to learn machine learning from a business perspective. It is highly practical and discusses in detail how a machine learning project should be deployed in real business applications. Not to be missed by any manager with a serious interest in AI and Machine Learning."Albert Bifet, Professor, Director of the AI Institute, The University of Waikato, New ZealandTable of ContentsPart 1: Understanding Machine Learning 1. Let's jump right in 2. Different kinds of ML 3. Creating ML models 4. Linear models 5. Neural networks 6. Tree-based approaches, ensembles and boosting 7. Dimensionality reduction and clustering 8. Unstructured data 9. Explainable AI Part 2: Managing Machine Learning Projects 10. The ML system lifecycle 11. The big picture 12. Creating value with ML 13. Making the business case 14. The ML pipeline 15. Development 16. Deployment and monitoring

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Book SynopsisIntroduction.- Propositional Logic.- First-order Predicate Logic.- Limitations of Logic.- Logic Programming with PROLOG.- Search, Games and Problem Solving.- Reasoning with Uncertainty.- Machine Learning and Data Mining.- Neural Networks.- Reinforcement Learning.- Solutions for the Exercises.

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Book SynopsisAgent-Based Models and the Economics of Crisis.- The Machine Learning perspective.- Setting up Agent-Based Models of Crisis (Microeconomic Model of Crisis; Virus on a Network Spread Model).- Developing  models with Python and R.

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Book SynopsisThis book explains how AI and Machine Learning can be applied to help businesses solve problems, support critical thinking and ultimately create customer value and increase profit.By considering business strategies, business process modeling, quality assurance, cybersecurity, governance and big data and focusing on functions, processes, and people's behaviors it helps businesses take a truly holistic approach to business optimization. It contains practical examples that make it easy to understand the concepts and apply them. It is written for practitioners (consultants, senior executives, decision-makers) dealing with real-life business problems on a daily basis, who are keen to develop systematic strategies for the application of AI/ML/BD technologies to business automation and optimization, as well as researchers who want to explore the industrial applications of AI and higher-level students. Table of ContentsForeword by Andy Lyman. Preface. Readers. Figures. Acknowledgments. Authors. 1 Artificial intelligence and machine learning: Opportunities for digital business. 2 Data to decisions: Evolving interrelationships. 3 Digital leadership: Strategies for AI adoption. 4 Machine learning types: Statistical understanding in the business context. 5 Dynamicity in learning: Smart selection of learning techniques. 6 Intelligent business processes with embedded analytics. 7 Adopting data-driven culture: Leadership and change management for business optimization. 8 Quality and risks: Assurance and control in BO. 9 Cybersecurity in BO: Significance and challenges for digital business. 10 Natural intelligence and social aspects of AI-based decisions. 11 Investing in the future technology of self-driving vehicles: Case study. Appendix A: Frameworks and libraries for ML. Appendix B: Datasets for ML and predictive analytics. Appendix C: AI and BO research areas. Index.

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Book SynopsisWhile many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems.

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Book SynopsisMIT presents a concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars. No in-depth knowledge of math or programming required!   Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don’t yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.   Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers:   • The evolution of machine learning • Important learning algorithms and example applications • Using machine learning algorithms for pattern recognition • Artificial neural networks inspired by the human brain • Algorithms that learn associations between instances • Reinforcement learning • Transparency, explainability, and fairness in machine learning • The ethical and legal implicates of data-based decision making   A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi.

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Book SynopsisPacked with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.Trade Review'Written by a great teacher who truly understands the material, the book is conversational and very approachable, while at the same time covering the material comprehensively. I really appreciated the organization, starting from fundamentals that the reader would know already, and then building knowledge structures from there.' Akhilesh Bajaj, The University of Tulsa'A much-needed book for learning and teaching the essentials of machine learning for practical usage. It has comprehensive and up-to-date coverage on the practical aspects of machine learning. The chapters on cloud computing and responsible AI cover two topics particularly relevant to today's machine learning practices, yet rarely found at such depth and quality in other machine learning books. This book is self-contained and highly accessible to readers of diverse backgrounds. Materials are organized into five easy-to-follow parts while striking a delicate balance between breadth and depth, and between theory and practice. I highly recommend this book to those who need/want to equip themselves with practical hand-on machine learning skills to get their work done.' Haiping Lu, University of Sheffield'… clearly and concisely introduces traditional and modern machine learning topics. The book is highly accessible for those who are very new to machine learning across diverse computing environments. Ethical issues that we need to pay more attention to are also discussed, and are a great feature.' Minwoo Lee, Department of Computer Science & School of Data Science, The University of North Carolina at Charlotte'…an accessible textbook for students of machine learning. The presentations of algorithms are clear and supported by examples. The conceptual questions at the end of each chapter allow students to review key concepts, while hands-on problems prepare students to apply what they have learned to real situations. Shah's book is also a valuable tool for practitioners of machine learning.' Tony Diana, Lecturer, University of Maryland Baltimore County (UMBC)'… an accessible yet far-reaching treatment of practical machine learning. Professor Shah leverages his years of experience creating, teaching, and applying machine learning, in academia as well as industry, to present material that ranges from classical topics to current trends. The pedagogy allows anyone - new or seasoned - to benefit by trying many hands-on problems in different application areas.' Rishabh Mehrotra, Director, Machine Learning at ShareChat'… an approachable exposition of machine learning with theories and context based on real-life, practical applications. Professor Shah interweaves theoretical concepts, such as dimensionality reduction, gradient descent, and reinforcement learning, with hands-on examples that are easy to understand. This helps students in the classroom as well as other engineering practitioners who are approaching these topics for real-world use cases.' Madhu Kurup, Vice President, Indeed.comTable of ContentsPart I. Basic Concepts: 1. Teaching computers to write programs; 2. Python; 3. Cloud computing; Part II. Supervised Learning: 4. Regression; 5. Classification-1; 6. Classification-2; Part III. Unsupervised Learning: 7. Clustering; 8. Dimensionality reduction; Part IV. Neural Networks: 9. Neural networks; 10. Deep learning; Part V. Further explorations: 11. Reinforcement learning; 12. Designing and evaluating ML systems; 13. Responsible AI; Appendices.

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Book SynopsisIntroduction to Machine Learning.- Foundations of Machine Learning.- Deep Learning and cloud computing.- Cloud Computing Concepts.- Practical Aspects in Machine Learning.- Artificial Intelligence and Information Security.- Examples of Analytics in the Cloud.- A case study of Data Collection and Analytics in the Cloud.- Future trends in Hardware based AI and ML.

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