Book Image

Apache Spark for Data Science Cookbook

By : Padma Priya Chitturi
Book Image

Apache Spark for Data Science Cookbook

By: Padma Priya Chitturi

Overview of this book

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Table of Contents (17 chapters)
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Many successful applications of machine learning exist already, including systems that analyse past sales data to predict customer behavior, optimizing robot behavior so that a task can be completed using minimum resources and extracting knowledge from bio-informatics data. With the advent of big data, maintaining large collections of data is one thing, but extracting useful information from these collections is even more challenging. The ML system should be able to scale on high volumes of data, the accuracy of the models built would also have to be quite high as the training takes place on large data.

Big data and machine learning take place in three steps-collecting, analyzing, and predicting. For this purpose, the Spark ecosystem supports a wide range of workloads including batch applications, iterative algorithms, interactive queries, and stream processing...