NitroFlare Python Data Science Cookbook

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    Python Data Science Cookbook by Gopi Subramanian
    English | 16 Nov. 2015 | ISBN: 1784396400 | 438 Pages | MOBI/EPUB/PDF (True) | 30.66 MB

    With: Code file


    This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience.

    Over 60 practical recipes to help you explore Python and its robust data science capabilities

    About This Book

    The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action
    Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python
    Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes

    What You Will Learn

    Explore the complete range of Data Science algorithms
    Get to know the tricks used by industry engineers to create the most accurate data science models
    Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively
    Create meaningful features to solve real-world problems
    Take a look at Advanced Regression methods for model building and variable selection
    Get a thorough understanding of the underlying concepts and implementation of Ensemble methods
    Solve real-world problems using a variety of different datasets from numerical and text data modalities
    Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on

    In Detail

    Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It's a disruptive technology changing the face of today's business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way.

    This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly.

    The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional.

    Style and approach

    This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.
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