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  • Book

  • Big data : principles and best practices of scalable real-time data systems

    • Authors: Nathan Marz (2015)

  • Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive- rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers. Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small tea...
  • Book

  • An Introduction to Machine Learning

    • Authors: Miroslav Kubat (2017)

  • This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
  • Book

  • Introduction To Machine Learning With Python

    • Authors: C. Müller, Andreas (2017)

  • You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
  • Book

  • An Introduction to Data:

    • Authors: Francesco Corea (2019)

  • This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a revie...
  • Book

  • Big Data and Health Analytics

    • Authors: Katherine Marconi (2015)

  • Data availability is surpassing existing paradigms for governing, managing, analyzing, and interpreting health data. Big Data and Health Analytics provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery. Written for health care professionals and executives, this is not a technical book on the use of statistics and machine-learning algorithms for extracting knowledge out of data, nor a book on the intricacies of database design. Instead, this book presents the current thinking of academic and industry researchers and leaders from around the world.
  • Book

  • Machine Learning with R: Expert Techniques for Predictive Modeling to Solve All Your Data Analysis Problems

    • Authors: Lantz, Brett (2016)

  • The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentat...
  • Book

  • Big data for dummies

    • Authors: Judith Hurwitz (2013)

  • Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.
  • Book

  • Analytics in a big data world : the essential guide to data science and its applications

    • Authors: Bart Baesens (2014)

  • The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.