As with many advanced and carefully designed technologies, people usually either love or hate R as a language. One of the reason being that R was one of the first language implementations that tries to manipulate complex objects, even though most of them turn out to be just a list as opposed to struct or map as in more mature modern implementations. R was originally created at the University of Auckland by Ross Ihaka and Robert Gentleman around 1993, and had its roots in the S language developed at Bell Labs around 1976, when most of the commercial programming was still done in Fortran. While R incorporates some functional features such as passing functions as a parameter and map/apply, it conspicuously misses some others such as lazy evaluation and list comprehensions. With all this said, R has a very good help system, and if someone says that they never had to go back to the help(…) command to figure out how to run a certain data transformation or model better, they...
Mastering Scala Machine Learning
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Mastering Scala Machine Learning
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Overview of this book
Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing.
This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees.
Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
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Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
Free Chapter
Exploratory Data Analysis
Data Pipelines and Modeling
Working with Spark and MLlib
Supervised and Unsupervised Learning
Regression and Classification
Working with Unstructured Data
Working with Graph Algorithms
Integrating Scala with R and Python
NLP in Scala
Advanced Model Monitoring
Index
Customer Reviews