Download or read book Machine Learning Concepts Tools And Data Visualization written by Minsoo Kang and published by World Scientific. This book was released on 2021-03-16 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book.
Download or read book Machine Learning Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2011-07-31 with total page 2174 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Download or read book No code Ai Concepts And Applications In Machine Learning Visualization And Cloud Platforms written by Minsoo Kang and published by World Scientific. This book was released on 2024-07-19 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a beginner-friendly guide to artificial intelligence (AI), ideal for those with no technical background. It introduces AI, machine learning, and deep learning basics, focusing on no-code methods for easy understanding. The book also covers data science, data mining, and big data processing, maintaining a no-code approach throughout. Practical applications are explored using no-code platforms like Microsoft Azure Machine Learning and AWS SageMaker. Readers are guided through step-by-step instructions and real-data examples to apply learning algorithms without coding. Additionally, it includes the integration of business intelligence tools like Power BI and AWS QuickSight into machine learning projects.This guide bridges the gap between AI theory and practice, making it a valuable resource for beginners in the field.
Download or read book Data Science in Societal Applications written by Siddharth Swarup Rautaray and published by Springer Nature. This book was released on 2022-09-15 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications.
Download or read book Data Science for Business Professionals written by Probyto Data Science and Consulting Pvt. Ltd. and published by BPB Publications. This book was released on 2020-05-06 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments
Download or read book No Code AI written by Sung Yul Park Myung-Ae Ch Min Soo Kang and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a beginner-friendly guide to artificial intelligence (AI), ideal for those with no technical background. It introduces AI, machine learning, and deep learning basics, focusing on no-code methods for easy understanding. The book also covers data science, data mining, and big data processing, maintaining a no-code approach throughout. Practical applications are explored using no-code platforms like Microsoft Azure Machine Learning and AWS SageMaker. Readers are guided through step-by-step instructions and real-data examples to apply learning algorithms without coding. Additionally, it includes the integration of business intelligence tools like Power BI and AWS QuickSight into machine learning projects. This guide bridges the gap between AI theory and practice, making it a valuable resource for beginners in the field.
Download or read book Machine Learning written by Thomas Laville and published by Createspace Independent Publishing Platform. This book was released on 2017-10-29 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thinking of learning more in Machine Learning? Then you have landed in the right place.The overall aim of this book is to explore and examine key concepts, methods and techniques used in the Machine Learning.Machines have come a part of our lives. No matter where you go, you will always find a machine around you, but it is only over the last few years that we have understood and enhanced the capabilities of machines. Machines can now perform tasks that involve the simulation of cognition, which is a task that until recently only human beings could accomplish. The ever-growing capabilities of machines are instilling a sense of fear among observers. What if machines became more intelligent than human beings become and decided to eliminate any human being who was not so smart as them?Machine learning is not as simple as turning a switch on and off. It is not an out-of-the-box solution either. Machines and statistical algorithms work in parallel with each other. This book will help you explore exactly what Machine learning is and will introduce the reader the concepts, techniques, application of Machine Learning Algorithms with the practical case studies and walk-through examples to practice. By the time you are done reading this book, you will have a complete understanding as to what Machine Learning is. Following are the important points discussed in this book: First Part: Introduction to Machine Learning Definition of Machine Learning and Artificial Intelligence Goals and importance of Machine Learning Using of Machine Learning Workkflow of Machine Learning Subjects involved in Machine Learning Second Part: Types of Machine Learning Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Third Part: Techniques and Algorithms of Machine Learning Linear Regression Modeling Decision Trees Bagging, Random Forest and Boosting Algorithms Principal Component Analysis K means K Nearest Neighbors Logistic Regression Na�ve Bayes Estimation Support Vector Machines Hierarchical Clustering Association Rules and Frequent Patterns Analysis Part 4: Problems in Machine Learning Overfitting and Underfitting Problems Bias and Variance Tradeoff The sparcity Problem Dinensionality Problem Data Problem Simplicity and Accuracy Book Objectives To have an appreciation for Machine Learning and an understanding of their fundamental principles. To have an elementary adeptness in a Machine Learning Concepts and terms which includes an ability to understand the algorithms. Target Users This book designed for a variety of target audiences. The most suitable users would include: Newbies in Computer Science Techniques and Artificial Intelligence Professionals in Data scientist and Social Sciences Professors, lecturers, or tutors to be in position to find better ways to explain the content to their students with simples and easiest way The students and Academicians, especially those that are focusing on Machine Learning as their professions Scroll to the top and click on 'buy now' to get started.
Download or read book DATA VISUALIZATION AND INTERPRETATION USING MACHINE LEARNING written by Anjan Kumar Reddy Ayyadapu and published by Xoffencerpublication. This book was released on 2024-04-18 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the various definitions of artificial intelligence, "machine-made intelligence" and "an artificial embodiment of some or all of the intellectual abilities possessed by humans" are two examples of what is meant by the term. Among the different explanations of artificial intelligence, the following are some essential points: "machines endowed with human-level intellect that can comprehend human-level reasoning, conduct, and thought processes." It is commonly believed that the ability to "apply prior knowledge and experience to achieve challenging new tasks" is what distinguishes a person as intelligent. One may make the case that this is a reference to the inherent wisdom that people possess in the end. In addition to being connected to the capacity for learning, this ability can be leveraged to respond in a flexible manner to a variety of situations and obstacles. A person's learning ability can be defined as their capability to learn new things in a short amount of time and in a comprehensive manner, or to acquire the same information in a more sophisticated manner. There is a correlation between prior knowledge and academic achievement, intellectual reasoning, and behavior; hence, intelligence may be molded via the process of being exposed to new material and training. It is for this reason that "the ability to solve problems" is frequently considered to be the starting point and ultimate definition of intelligence. When it comes to addressing a wide variety of problems, we require individuals who possess a high level of intelligence. Consider the game of chess as an illustration. You'll need to draw on knowledge from a broad variety of sources, such as books, internet resources, and other players, in order to make accurate guesses and put them into action. In order to carry out these acts, a high level of cognitive capacity is required, and it is via intelligencebased learning that new ways of thinking are developed. "Thought" is defined as "consciousness" in scientific contexts, which in turn characterize it as "experience" of an object in its whole.
Download or read book Data Visualization written by Alex Campbell and published by . This book was released on 2020-10-14 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a budding data scientist or aspire to be one someday? Have you ever wondered about all the data that is constantly in motion around the world? Does it surprise you when Netflix gives you suggestions for your next movie and it is very close to your taste in movies? Would you like to know more about data and how it is used regularly to influence every action you take? Do you want to know how businesses with a turnover in millions make critical decisions to make or break their business? Do you wonder how humans can process huge data for their decision-making? All this can be achieved through data in the form of visual representations. If you are curious to know the answers to all these questions, then this is the right book for you. This book will introduce how data is converted into visuals for better interpretation using the programming language known as Python. If you are well versed with Python, you will easily transition into leveraging the tools available to you in Python to create appealing data visuals from a raw set of data. You will also learn to create your own machine learning models in Python to create data visualizations that will ease decision-making for you or your organization. If you are looking to launch yourself in the world of data science and looking to use Python as the most used tool in your toolkit, this book will serve as the perfect launchpad. This book is designed to help individuals with basic Python programming knowledge to learn something new concerning the use of Python data visualization libraries in the data science domain. The tools in this book will help you get a first impression of data science and how Python can be used extensively to create beautiful visuals to turn raw data into stories. The book will take you through: The need for data visualization today The concepts and techniques of data visualization The various tools available to achieve data visualization Data visualization libraries in Python The Pareto Chart Regression and Classification using Python This book has been designed for you to understand data visualization using Python. There are step by step guides and images with code snippets throughout the book to help you get your hands dirty by creating your own data visuals. So, here's hoping that this book helps you find your appetite to become a data scientist with a mystery in presenting data through effective visualizations someday. Click the Buy Now button to get started!
Download or read book Machine Learning for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2023-05-02 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING FOR BUSINESS ANALYTICS An up-to-date introduction to a market-leading platform for data analysis and machine learning Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. readers will also find: Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom Four new chapters, covering topics including Text Mining and Responsible Data Science An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook A guide to JMP Pro®’s new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.
Download or read book Machine Learning with TensorFlow Second Edition written by Mattmann A. Chris and published by Manning Publications. This book was released on 2021-02-02 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape
Download or read book Machine Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2016-05-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!
Download or read book Machine Learning Concepts Tools and Techniques written by Ivy Wright and published by . This book was released on 2022-09-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of computer algorithms that improve automatically through experience and by the use of data is referred to as machine learning. It is considered to be a part of artificial intelligence. Data mining is a related area of study, focusing on exploratory data analysis through unsupervised learning. Performing machine learning involves creating a model. Some of the different types of models that are used and researched for machine learning systems are artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks and genetic algorithms. There are many applications for machine learning such as agriculture, banking, economics, marketing, medical diagnosis, telecommunication, software engineering, time series forecasting and bioinformatics. As this field is emerging at a rapid pace, the contents of this book will help the readers understand the modern concepts and applications of the subject. It provides comprehensive insights into the field of machine learning. This book is a collective contribution of a renowned group of international experts.
Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
Download or read book Data Science Fundamentals and Practical Approaches written by Dr. Gypsy Nandi and published by BPB Publications. This book was released on 2020-06-02 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics
Download or read book Machine Learning for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2023-03-08 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Business Analytics Machine learning—also known as data mining or data analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
