At this stage, we have only a raw text representing reviews, which is not sufficient to run any machine learning algorithm. We need to transform the text into a numeric format, aka perform the so-called "feature extraction" (it is as it sounds; we are taking the input data and extracting features which we will use to train a model). The method generates some new features based on input feature(s). There are many methods regarding how the text can be transformed into numeric features. We can count the number of words, length of text, or number of punctuations. However, to represent text in a systematic way that would reflect a text structure, we need more elaborate methods.
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