Raw data for problems often comes from multiple sources with different and often incompatible formats. The beauty of the Spark programming model is its ability to define data operations that process the incoming data and transform it into a regular form that can be used for further feature engineering and model building. This process is commonly referred to as data munging and is where much of the battle is won with respect to data science projects. We keep this section intentionally brief because the best way to showcase the power--and necessity!--of data munging is by example. So, take heart; we have plenty of practice to go through in this book, which emphasizes this essential process.
Mastering Machine Learning with Spark 2.x
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Mastering Machine Learning with Spark 2.x
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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.
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Current Title:
Mastering Machine Learning with Spark 2.x
Table of Contents (9 chapters)
Preface
Free Chapter
Introduction to Large-Scale Machine Learning and Spark
Detecting Dark Matter - The Higgs-Boson Particle
Ensemble Methods for Multi-Class Classification
Predicting Movie Reviews Using NLP and Spark Streaming
Word2vec for Prediction and Clustering
Extracting Patterns from Clickstream Data
Graph Analytics with GraphX
Lending Club Loan Prediction
Customer Reviews