Data Mining With Matlab Classification Predictive Techniques

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Data Mining and Analysis in the Engineering Field

Author : Bhatnagar, Vishal
Publisher : IGI Global
ISBN 13 : 1466660872
Total Pages : 433 pages
Book Rating : 4.4/5 (666 download)

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Book Synopsis Data Mining and Analysis in the Engineering Field by : Bhatnagar, Vishal

Download or read book Data Mining and Analysis in the Engineering Field written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2014-05-31 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particularly in the fields of software engineering, virtual reality, and computer science, data mining techniques play a critical role in the success of a variety of projects and endeavors. Understanding the available tools and emerging trends in this field is an important consideration for any organization. Data Mining and Analysis in the Engineering Field explores current research in data mining, including the important trends and patterns and their impact in fields such as software engineering. With a focus on modern techniques as well as past experiences, this vital reference work will be of greatest use to engineers, researchers, and practitioners in scientific-, engineering-, and business-related fields.

Machine Learning for Healthcare Applications

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119791812
Total Pages : 418 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning for Healthcare Applications by : Sachi Nandan Mohanty

Download or read book Machine Learning for Healthcare Applications written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2021-04-13 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB

Download ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB PDF Online Free

Author : CESAR PERZ LOPEZ
Publisher : SCIENTIFIC BOOKS
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4./5 ( download)

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Book Synopsis ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB by : CESAR PERZ LOPEZ

Download or read book ARTIFICIAL INTELLIGENCE ALGORITHMS FOR UNSUPERVISED LEARNING: CLUSTERING AND PATTERN RECOGNITION WITH NEURAL NETWORKS. Examples with MATLAB written by CESAR PERZ LOPEZ and published by SCIENTIFIC BOOKS. This book was released on with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. On the other hand, unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. Some examples of unsupervised learning algorithms include clustering and association rules. In the case of this type of learning, there is no pre-training data set; the problem is approached blindly and only with logical operations to guide it. Although at first glance it seems impossible, it is about the ability to solve complex problems using only input data and logical algorithms. This avoids the use of reference data. Unsupervised learning algorithms are used to discover hidden patterns in unlabeled data. Unlike supervised learning algorithms, where there is prior knowledge of the desired answers, these algorithms do not have a set of ordered data. They are responsible for determining the most important common characteristics of a group of information and then grouping them according to their similarities. Among the most interesting models are the neural networks. MATLAB implementrs the Deep Learning Toolbox specialized in the techniques of analytics based on neural networks. Throughout this book the techniques of analytics for clustering and classification based on neural networks are developed using MATLAB software

Lecture Notes in Data Mining

Author : Michael W. Berry
Publisher : World Scientific
ISBN 13 : 9812773630
Total Pages : 238 pages
Book Rating : 4.8/5 (127 download)

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Book Synopsis Lecture Notes in Data Mining by : Michael W. Berry

Download or read book Lecture Notes in Data Mining written by Michael W. Berry and published by World Scientific. This book was released on 2006 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited OC student-authored lecturesOCO which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms. The book''s discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining. Sample Chapter(s). Chapter 1: Point Estimation Algorithms (397 KB). Contents: Point Estimation Algorithms; Applications of Bayes Theorem; Similarity Measures; Decision Trees; Genetic Algorithms; Classification: Distance Based Algorithms; Decision Tree-Based Algorithms; Covering (Rule-Based) Algorithms; Clustering: An Overview; Clustering Hierarchical Algorithms; Clustering Partitional Algorithms; Clustering: Large Databases; Clustering Categorical Attributes; Association Rules: An Overview; Association Rules: Parallel and Distributed Algorithms; Association Rules: Advanced Techniques and Measures; Spatial Mining: Techniques and Algorithms. Readership: An introductory data mining textbook or a technical data mining book for an upper level undergraduate or graduate level course."

Predictive Intelligence Using Big Data and the Internet of Things

Author : Gupta, P.K.
Publisher : IGI Global
ISBN 13 : 1522562117
Total Pages : 316 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Predictive Intelligence Using Big Data and the Internet of Things by : Gupta, P.K.

Download or read book Predictive Intelligence Using Big Data and the Internet of Things written by Gupta, P.K. and published by IGI Global. This book was released on 2018-12-28 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers.

MATLAB for Machine Learning

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1788399390
Total Pages : 374 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis MATLAB for Machine Learning by : Giuseppe Ciaburro

Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.

Business Intelligence: Concepts, Methodologies, Tools, and Applications

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1466695633
Total Pages : 2326 pages
Book Rating : 4.4/5 (666 download)

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Book Synopsis Business Intelligence: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

Download or read book Business Intelligence: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2015-12-29 with total page 2326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries.

Artificial Intelligence and Industrial Applications

Author : Tawfik Masrour
Publisher : Springer Nature
ISBN 13 : 3031435206
Total Pages : 493 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Artificial Intelligence and Industrial Applications by : Tawfik Masrour

Download or read book Artificial Intelligence and Industrial Applications written by Tawfik Masrour and published by Springer Nature. This book was released on 2023-10-16 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Amid the dynamic growth of artificial intelligence, this book presents a collection of findings and advancements from the second edition of the A2IA-Artificial Intelligence and Industrial Applications conference. The conference, hosted by ENSAM-Meknès at Moulay Ismail University, Morocco, fosters knowledge exchange in AI, focusing primarily on its industrial applications. Covering a wide range of topics, the book highlights the adaptable nature of AI and its increasing impact on industrial sectors. It brings together contributions from an international cohort of researchers, discussing themes such as intelligent manufacturing and maintenance, intelligent supply chain management, various modes of learning including supervised, unsupervised, reinforcement, semi-supervised, and graph-based, as well as neural networks, deep learning, planning, and optimization. A defining feature of this edition is its extensive scope and emphasis on the practical applications of AI, along with its foundational elements. It facilitates an understanding of AI's current state and potential future direction, showcasing recent developments that bridge the gap between theory and practice. Designed for a diverse readership, this book is of interest to AI practitioners, academics, and enthusiasts, as well as to those new to the field. It provides an opportunity to explore AI's critical role in industrial applications, and the practical insights it offers are likely to be beneficial for decision-making within industrial settings.

Advances in Data Science and Management

Author : Samarjeet Borah
Publisher : Springer Nature
ISBN 13 : 9811656851
Total Pages : 620 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Advances in Data Science and Management by : Samarjeet Borah

Download or read book Advances in Data Science and Management written by Samarjeet Borah and published by Springer Nature. This book was released on 2022-02-13 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality papers presented at the Second International Conference on Data Science and Management (ICDSM 2021), organized by the Gandhi Institute for Education and Technology, Bhubaneswar, from 19 to 20 February 2021. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.

Introduction to Data Mining

Author : Pang-Ning Tan
Publisher : Pearson Education India
ISBN 13 : 9332586055
Total Pages : 781 pages
Book Rating : 4.3/5 (325 download)

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Book Synopsis Introduction to Data Mining by : Pang-Ning Tan

Download or read book Introduction to Data Mining written by Pang-Ning Tan and published by Pearson Education India. This book was released on 2016 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni

Data Mining

Author : Florin Gorunescu
Publisher : Springer Science & Business Media
ISBN 13 : 3642197213
Total Pages : 364 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Data Mining by : Florin Gorunescu

Download or read book Data Mining written by Florin Gorunescu and published by Springer Science & Business Media. This book was released on 2011-03-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.

Information Systems Design and Intelligent Applications

Author : Suresh Chandra Satapathy
Publisher : Springer
ISBN 13 : 8132227522
Total Pages : 749 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Information Systems Design and Intelligent Applications by : Suresh Chandra Satapathy

Download or read book Information Systems Design and Intelligent Applications written by Suresh Chandra Satapathy and published by Springer. This book was released on 2016-02-02 with total page 749 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third international conference on INformation Systems Design and Intelligent Applications (INDIA – 2016) held in Visakhapatnam, India during January 8-9, 2016. The book covers all aspects of information system design, computer science and technology, general sciences, and educational research. Upon a double blind review process, a number of high quality papers are selected and collected in the book, which is composed of three different volumes, and covers a variety of topics, including natural language processing, artificial intelligence, security and privacy, communications, wireless and sensor networks, microelectronics, circuit and systems, machine learning, soft computing, mobile computing and applications, cloud computing, software engineering, graphics and image processing, rural engineering, e-commerce, e-governance, business computing, molecular computing, nano-computing, chemical computing, intelligent computing for GIS and remote sensing, bio-informatics and bio-computing. These fields are not only limited to computer researchers but also include mathematics, chemistry, biology, bio-chemistry, engineering, statistics, and all others in which computer techniques may assist.

Data Mining in Agriculture

Author : Antonio Mucherino
Publisher : Springer Science & Business Media
ISBN 13 : 0387886141
Total Pages : 284 pages
Book Rating : 4.3/5 (878 download)

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Book Synopsis Data Mining in Agriculture by : Antonio Mucherino

Download or read book Data Mining in Agriculture written by Antonio Mucherino and published by Springer Science & Business Media. This book was released on 2009-08-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Classification as a Tool for Research

Author : Hermann Locarek-Junge
Publisher : Springer Science & Business Media
ISBN 13 : 3642107451
Total Pages : 825 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Classification as a Tool for Research by : Hermann Locarek-Junge

Download or read book Classification as a Tool for Research written by Hermann Locarek-Junge and published by Springer Science & Business Media. This book was released on 2010-08-03 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering and Classification, Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of statistics, mathematics, computer science and artificial intelligence. They cover general methods and techniques that can be applied to a vast set of applications such as in business and economics, marketing and finance, engineering, linguistics, archaeology, musicology, biology and medical science. This volume contains the revised versions of selected papers presented during the 11th Biennial IFCS Conference and 33rd Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was organized in cooperation with the International Federation of Classification Societies (IFCS), and was hosted by Dresden University of Technology, Germany, in March 2009.

Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development

Author : Sandeep Kumar
Publisher : CRC Press
ISBN 13 : 1000727033
Total Pages : 127 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development by : Sandeep Kumar

Download or read book Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development written by Sandeep Kumar and published by CRC Press. This book was released on 2019-11-11 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world. Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world. Key Features: Highlights the importance and applications of Swarm Intelligence and Evolutionary Algorithms in Healthcare industry. Elaborates Swarm Intelligence and Evolutionary Algorithms for Cancer Detection. In-depth coverage of computational methodologies, approaches and techniques based on Swarm Intelligence and Evolutionary Algorithms for detecting Brain Tumour including deep learning to optimize brain tumor diagnosis. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms. Focuses on applying Swarm Intelligence and Evolutionary Algorithms for Heart Disease detection and diagnosis. Comprehensively covers the role of Swarm Intelligence and Evolutionary Algorithms for Drug Design and Discovery. The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.

Computational Intelligence in Data Science

Author : Vallidevi Krishnamurthy
Publisher : Springer Nature
ISBN 13 : 3030926001
Total Pages : 229 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Computational Intelligence in Data Science by : Vallidevi Krishnamurthy

Download or read book Computational Intelligence in Data Science written by Vallidevi Krishnamurthy and published by Springer Nature. This book was released on 2021-12-11 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the Fourth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2021, held in Chennai, India, in March 2021. The 20 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science.

Handbook of Artificial Intelligence

Author : Dumpala Shanthi
Publisher : Bentham Science Publishers
ISBN 13 : 9815124528
Total Pages : 297 pages
Book Rating : 4.8/5 (151 download)

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Book Synopsis Handbook of Artificial Intelligence by : Dumpala Shanthi

Download or read book Handbook of Artificial Intelligence written by Dumpala Shanthi and published by Bentham Science Publishers. This book was released on 2023-11-13 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector. This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with an introduction to AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry use-case demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more. Additional features of the book include a summary and references in every chapter, and several full-color images to visualize concepts for easy understanding. It is an ideal handbook for both students and instructors in undergraduate level courses in artificial intelligence, data science, engineering and computer science who are required to understand AI/ML in a practical context.