Data Science And Machine Learning Demystified
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Big Data Demystified
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Author : David Stephenson
language : en
Publisher: FT Press
Release Date : 2018-02-12
Big Data Demystified written by David Stephenson and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-12 with categories.
Big Data is a big topic, based on simple principles. Guided by leading expert in the field, David Stephenson, you will be amazed at how you can transform your company, and significantly improve KPIs across a broad range of business units and applications. Find out how an ecommerce company avoided two million product returns per year, how a newspaper saw triple-digit annual growth in digital subscriptions, how researchers in England learned to better detect pending cardiovascular problems, and how AI programs taught themselves to win games using techniques that even their human programmers didn't understand, all thanks to big data. Find out also how one company realized it could swap a million dollar hardware system with a twenty thousand dollar replacement. With simple and straightforward chapters that allow you to map examples onto your own business, Big Data Demystified will help you: · Know which data is most useful to collect now and why it's important to start collecting that data as soon as possible. · Understand big data and data science and how they can help you reach your business goals and gain competitive advantage. · Use big data to understand where you are now and how you can improve in the future. · Understand factors in choosing a big data system, including whether to go with cloud-based solutions. · Construct your big data team in a way that supports an effective strategy and helps make your business more data-driven. BIG DATA MAKES A BIG DIFFERENCE "Read this book! It is an essential guide to using data in a practical way that drives results." Ian McHenry, CEO Beyond Pricing "This is the book we've been missing: big data explained without the complexity." Marc Salomon, Professor in Decision Sciences and Dean at University of Amsterdam Business School "Big Data for the rest of us! I have never come across a book that is so full of practical advice, actionable examples and helpful explanations. Read this one book and start executing Big Data at your workplace tomorrow!" Tobias Wann CEO at @Leisure Group
Machine Learning Demystified
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Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2025-06-21
Machine Learning Demystified written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-21 with Computers categories.
Unlock the potential of the digital future with "Machine Learning Demystified," a comprehensive guide that simplifies the complex world of artificial intelligence. Designed for learners at every level, this eBook transforms intricate machine learning concepts into digestible insights, empowering you to harness the power of AI across diverse industries. Beginning with an introduction to the world of machine learning and the pivotal role it plays in the evolution of artificial intelligence, the book guides you through fundamental concepts like supervised, unsupervised, and reinforcement learning. Each section breaks down sophisticated topics into clear, understandable lessons. Dive into key algorithms like decision trees, linear regression, and neural networks, with dedicated chapters that walk you through the architecture and training of neural nets. Explore what sets deep learning apart and discover its exciting applications, from healthcare innovations to cutting-edge finance solutions, and beyond. "Machine Learning Demystified" equips you with practical tools for handling and preprocessing data, ensuring data quality and augmentation are well understood. Learn to evaluate model performance and tackle common challenges, such as avoiding overfitting and ensuring cross-validation. Beyond technical prowess, this eBook addresses ethical considerations, emphasizing the importance of bias mitigation, privacy concerns, and transparency in AI systems. Further, explore the rapidly evolving landscape of machine learning technologies, from popular libraries to emerging cloud-based solutions. Examine real-world case studies showcasing innovative uses of machine learning across business, technology, and the public sector. Discover future trends like AutoML and quantum machine learning, directing you towards the future trajectory of AI. Whether you are getting started on your journey or building a personalized learning path, "Machine Learning Demystified" offers valuable resources, communities, and insights to support your ongoing exploration. Reflect on the transformative impact of simplified machine learning and embrace a journey of knowledge empowerment and discovery.
Choosing Chinese Universities
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Author : Alice Y.C. Te
language : en
Publisher: Routledge
Release Date : 2022-10-07
Choosing Chinese Universities written by Alice Y.C. Te and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-07 with Education categories.
This book unpacks the complex dynamics of Hong Kong students’ choice in pursuing undergraduate education at the universities of Mainland China. Drawing on an empirical study based on interviews with 51 students, this book investigates how macro political/economic factors, institutional influences, parental influence, and students’ personal motivations have shaped students’ eventual choice of university. Building on Perna’s integrated model of college choice and Lee’s push-pull mobility model, this book conceptualizes that students’ border crossing from Hong Kong to Mainland China for higher education is a trans-contextualized negotiated choice under the "One Country, Two Systems" principle. The findings reveal that during the decision-making process, influencing factors have conditioned four archetypes of student choice: Pragmatists, Achievers, Averages, and Underachievers. The book closes by proposing an enhanced integrated model of college choice that encompasses both rational motives and sociological factors, and examines the theoretical significance and practical implications of the qualitative study. With its focus on student choice and experiences of studying in China, this book’s research and policy findings will interest researchers, university administrators, school principals, and teachers.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Machine Learning Demystified A Practical Guide To Building Smarter Systems
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Author : Guillaume Lessard
language : en
Publisher: iD01t Productions
Release Date : 2025-01-14
Machine Learning Demystified A Practical Guide To Building Smarter Systems written by Guillaume Lessard and has been published by iD01t Productions this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-14 with Computers categories.
Machine Learning Demystified: A Practical Guide to Building Smarter Systems Unlock the power of Artificial Intelligence with Machine Learning Demystified, a comprehensive and beginner-friendly guide to understanding and applying machine learning principles in real-world scenarios. Authored by Guillaume Lessard, this book bridges the gap between complex algorithms and practical applications, making AI accessible to everyone—from curious beginners to seasoned professionals. What Awaits Inside? Simplified Learning: Master the fundamentals of machine learning with relatable examples and step-by-step tutorials. Algorithm Essentials: Explore supervised and unsupervised learning, neural networks, natural language processing, and computer vision. Hands-On Projects: Dive into real-world applications like spam detection, housing price prediction, and image recognition systems. Practical Tools: Gain expertise in industry-standard frameworks such as TensorFlow, PyTorch, and Scikit-Learn. Ethical AI Practices: Learn to build responsible AI systems with fairness, transparency, and privacy in mind. Why Choose This Book? Many AI resources overwhelm readers with heavy mathematics and jargon. This guide adopts a unique approach, focusing on practical, actionable insights that empower you to create smarter systems. Whether you’re exploring AI's potential for business or personal projects, this book provides the clarity and confidence needed to succeed. Who Is It For? Aspiring data scientists and developers Business professionals exploring AI-driven solutions Anyone passionate about the future of technology Embark on a journey to demystify machine learning and turn your ideas into reality. Your smarter systems start here!
Data Science And Machine Learning Demystified
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Author : Liam Stone
language : en
Publisher:
Release Date : 2024-01-12
Data Science And Machine Learning Demystified written by Liam Stone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-12 with Computers categories.
The union of data science and machine learning has become a powerful force, transforming industries and altering how we interact with the outside world in an era characterized by data-driven decision-making. Welcome to "Data Science and Machine Learning Demystified: Mastering Data Science and Machine Learning - Advanced Techniques and Applications." This extensive e-book takes you on a journey into the depths of innovative techniques, revealing the complex web that unites machine learning and data science in their most potent forms. There has never been a greater pressing need for advanced tools and strategies to extract useful insights from the massive volumes of data that are flooding around the world. This e-book is intended to serve as your guide through this complicated environment, giving you the information and abilities needed to take on challenging data problems head-on. We recognize that you are not a newcomer to the field of machine learning and data science. You've already experienced success with simple models and understood the fundamentals. But the goal of this e-book is not to only scratch the surface; rather, it is to delve further, discover new ground, and fully utilize these technologies.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Machine Learning And Data Science In The Oil And Gas Industry
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Author : Patrick Bangert
language : en
Publisher: Gulf Professional Publishing
Release Date : 2021-03-04
Machine Learning And Data Science In The Oil And Gas Industry written by Patrick Bangert and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Science categories.
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Demystifying Big Data Machine Learning And Deep Learning For Healthcare Analytics
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Author : Pradeep N
language : en
Publisher: Academic Press
Release Date : 2021-06-10
Demystifying Big Data Machine Learning And Deep Learning For Healthcare Analytics written by Pradeep N and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-10 with Science categories.
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation