Book Image

Mastering Machine Learning with Spark 2.x

By : Michal Malohlava, Alex Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Michal Malohlava, Alex Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
3
Ensemble Methods for Multi-Class Classification

Summary

In this chapter, we wanted to give you a brief glimpse into the life of a data scientist, what this entails, and some of the challenges that data scientists consistently face. In light of these challenges, we feel that the Apache Spark project is ideally positioned to help tackle these topics, which range from data ingestion and feature extraction/creation to model building and deployment. We intentionally kept this chapter short and light on verbiage because we feel working through examples and different use cases is a better use of time as opposed to speaking abstractly and at length about a given data science topic. Throughout the rest of this book, we will focus solely on this process while giving best-practice tips and recommended reading along the way for users who wish to learn more. Remember that before embarking on your next data science project, be sure to clearly define the problem beforehand, so you can ask an intelligent question of your data and (hopefully) get an intelligent answer!

One awesome website for all things data science is KDnuggets (http://www.kdnuggets.com). Here's a great article on the language all data scientists must learn in order to be successful (http://www.kdnuggets.com/2015/09/one-language-data-scientist-must-master.html).