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Python Data Science Cookbook

Python programiranje Python programiranje

Python Data Science Cookbook

Autor: Gopi Subramanian
Broj strana: 438
ISBN broj: 9781784396404
Godina izdanja: 2015.

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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

Who This Book Is For

Whether novice or expert, this book is packed with the practical tips and techniques that data science pros need to succeed with Python. From fundamental data science techniques to advanced functions that you didn't even know Python could do, this book is the essential desktop guide for anyone doing more with data using Python.

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.


Gopi Subramanian

Gopi Subramanian is a data scientist with over 15 years of experience in the field of data mining and machine learning. During the past decade, he has designed, conceived, developed, and led data mining, text mining, natural language processing, information extraction and retrieval, and search systems for various domains and business verticals, including engineering infrastructure, consumer finance, healthcare, and materials. In the loyalty domain, he has conceived and built innovative consumer loyalty models and designed enterprise-wide systems for personalized promotions. He has filed over ten patent applications at the US and Indian patent office and has several publications to his credit. He currently lives and works in Bangaluru, India.

Table of Contents

Chapter 1: Python for Data Science
Chapter 2: Python Environments
Chapter 3: Data Analysis – Explore and Wrangle
Chapter 4: Data Analysis – Deep Dive
Chapter 5: Data Mining – Needle in a Haystack
Chapter 6: Machine Learning 1
Chapter 7: Machine Learning 2
Chapter 8: Ensemble Methods
Chapter 9: Growing Trees
Chapter 10: Large-Scale Machine Learning – Online Learning


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