Book Image

Learning Data Mining with Python - Second Edition

By : Robert Layton
Book Image

Learning Data Mining with Python - Second Edition

By: Robert Layton

Overview of this book

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

MapReduce


There are a number of concepts to perform data mining and general computation on big data. One of the most popular is the MapReduce model, which can be used for general computation on arbitrarily large datasets.

MapReduce originates from Google, where it was developed with distributed computing in mind. It also introduces fault tolerance and scalability improvements. The original research for MapReduce was published in 2004, and since then there have been thousands of projects, implementations, and applications using it.

While the concept is similar to many previous concepts, MapReduce has become a staple in big data analytics.

There are two major stages in a MapReduce job.

  1. The first is Map, by which we take a function and a list of items, and apply that function to each item. Put another way, we take each item as the input to the function and store the result of that function call:
  1. The second step is Reduce, where we take the results from the map step and combine them using a function...