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Mastering Predictive Analytics with Python by Joseph Babcock ebook EPUB, FB2, PDF

9781785882715
English

1785882716
Exploit the power of data in your business by building advanced predictive modeling applications with PythonAbout This Book- Master open source Python tools to build sophisticated predictive models- Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide- Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.What You Will Learn - Gain an insight into components and design decisions for an analytical application- Master the use Python notebooks for exploratory data analysis and rapid prototyping- Get to grips with applying regression, classification, clustering, and deep learning algorithms- Discover the advanced methods to analyze structured and unstructured data- Find out how to deploy a machine learning model in a production environment- Visualize the performance of models and the insights they produce- Scale your solutions as your data grows using Python- Ensure the robustness of your analytic applications by mastering the best practices of predictive analysisIn DetailThe volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to lifeStyle and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting., Exploit the power of data in your business by predicting probabilities and trends and creating advanced analytic solutions with PythonAbout This Book* Master the use of open source Python tools to build sophisticated predictive models* Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide* Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.What You Will Learn* Gain an insight into components and design decisions for an analytical application* Master the use Python notebooks for exploratory data analysis and rapid prototyping* Get to grips with applying regression, classification, clustering, and deep learning algorithms* Discover the advanced methods to analyze structured and unstructured data* Find out how to deploy a machine learning model in a production environment* Visualize the performance of models and the insights they produce* Scale your solutions as your data grows using Python* Ensure the robustness of your analytic applications by mastering the best practices of predictive analysisIn DetailThe volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.In Mastering Predictive Analytics with Python, you will work through a step-by-step process to turn raw data into powerful insights. Power-packed with case studies and code examples using popular open-source Python libraries, this volume illustrates the complete development process for analytic applications. The detailed examples illustrate robust and scalable applications for common use cases. You will learn to quickly apply these methods to your own data.Covering a wide range of algorithms for classification, regression, clustering, and cutting-edge techniques such as deep learning, you will learn not only how these methods work, but how to implement them in practice. You will also gain the skill to choose the right approach for your problem. This guide also explains how to develop engaging visualizations from these algorithms, to bring the insights of predictive modeling to life for the analyst and their stakeholders.

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The strength of EHW hinges on the synergy between these technologies supported by the advanced analogue and digital programmable circuits.The main economic activity in the city is tourism.The book starts by presenting the different two-degree-of-freedom PID control algorithm variations and their conversion relations as well as the indexes used for performance, robustness and fragility evaluation: the bases of the proposed model.Indeed each one of us is, and will be, touched by this far-reaching transformation in food and agriculture; in the pharmaceutical, chemical, and cosmetics industries; in film, television, and live entertainment; in tourism and wildlife management; in the pet trade for dogs and cats and exotic wildlife; and in fur and leather fashions.In The Humane Economy, Wayne Pacelle, President/CEO of the Human Society of the United States, explores how our everyday economic decisions impact the survival and wellbeing of animals, and how we can make choices that better support them.The material covered is organized into ?ve broad categories: Models: The material covered includes data abstraction and the relational data model (Chapter 1), the constraint data model (Chapter 2), several s- tiotemporal data models (Chapter 13.The Workshop provided an ideal forum for academic and industrial researchers to discuss their experience and research results in this field.Equations of motion of such flexible multibody systems are derived in this book by using Kane's method, for reasons of reduced labor and simplicity of the equations possible with a choice of variables.It equips the practicing engineer with knowledge of flexible multibody dynamics algorithms that provide accurate results in reduced simulation time.Kline argues that, for about twenty years after 1950, the growth of cybernetics and information theory and ever-more-powerful computers produced a utopian information narrative--an enthusiasm for information science that influenced natural scientists, social scientists, engineers, humanists, policymakers, public intellectuals, and journalists, all of whom struggled to come to grips with new relationships between humans and intelligent machines.Kline traces the relationship between the invention of computers and communication systems and the rise, decline, and transformation of cybernetics by analyzing the lives and work of such notables as Norbert Wiener, Claude Shannon, Warren McCulloch, Margaret Mead, Gregory Bateson, and Herbert Simon.Andrew Boyd, George Booth, and Charles Harvey 287 1 Introduction 288 2 The Traffic Flow Management (TFM) Problem 289 3 Recent TFM Optimization Models 292 4 The Time Assignment Model (TAM) 302 5 Summary and Conclusions 307 REFERENCES 309 11 THE PROCESSES OF AIRLINE SYSTEM OPERATIONS CONTROL Seth C.