Mastering Scala Machine Learning

Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop
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Mastering Scala Machine Learning

Alex Kozlov

1 customer reviews
Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop
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Book Details

ISBN 139781785880889
Paperback310 pages

Book Description

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.

Table of Contents

Chapter 1: Exploratory Data Analysis
Getting started with Scala
Distinct values of a categorical field
Summarization of a numeric field
Basic, stratified, and consistent sampling
Working with Scala and Spark Notebooks
Basic correlations
Summary
Chapter 2: Data Pipelines and Modeling
Influence diagrams
Sequential trials and dealing with risk
Exploration and exploitation
Unknown unknowns
Basic components of a data-driven system
Optimization and interactivity
Summary
Chapter 3: Working with Spark and MLlib
Setting up Spark
Understanding Spark architecture
Applications
ML libraries
Spark performance tuning
Running Hadoop HDFS
Summary
Chapter 4: Supervised and Unsupervised Learning
Records and supervised learning
Unsupervised learning
Problem dimensionality
Summary
Chapter 5: Regression and Classification
What regression stands for?
Continuous space and metrics
Linear regression
Logistic regression
Regularization
Multivariate regression
Heteroscedasticity
Regression trees
Classification metrics
Multiclass problems
Perceptron
Generalization error and overfitting
Summary
Chapter 6: Working with Unstructured Data
Nested data
Other serialization formats
Hive and Impala
Sessionization
Working with traits
Working with pattern matching
Other uses of unstructured data
Probabilistic structures
Projections
Summary
Chapter 7: Working with Graph Algorithms
A quick introduction to graphs
SBT
Graph for Scala
GraphX
Summary
Chapter 8: Integrating Scala with R and Python
Integrating with R
Integrating with Python
Summary
Chapter 9: NLP in Scala
Text analysis pipeline
MLlib algorithms in Spark
Segmentation, annotation, and chunking
POS tagging
Using word2vec to find word relationships
Summary
Chapter 10: Advanced Model Monitoring
System monitoring
Summary

What You Will Learn

  • Sharpen your functional programming skills in Scala using REPL
  • Apply standard and advanced machine learning techniques using Scala
  • Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data
  • Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data
  • Understand the principles of supervised and unsupervised learning in machine learning
  • Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet
  • Construct reliable and robust data pipelines and manage data in a data-driven enterprise
  • Implement scalable model monitoring and alerts with Scala

Authors

Table of Contents

Chapter 1: Exploratory Data Analysis
Getting started with Scala
Distinct values of a categorical field
Summarization of a numeric field
Basic, stratified, and consistent sampling
Working with Scala and Spark Notebooks
Basic correlations
Summary
Chapter 2: Data Pipelines and Modeling
Influence diagrams
Sequential trials and dealing with risk
Exploration and exploitation
Unknown unknowns
Basic components of a data-driven system
Optimization and interactivity
Summary
Chapter 3: Working with Spark and MLlib
Setting up Spark
Understanding Spark architecture
Applications
ML libraries
Spark performance tuning
Running Hadoop HDFS
Summary
Chapter 4: Supervised and Unsupervised Learning
Records and supervised learning
Unsupervised learning
Problem dimensionality
Summary
Chapter 5: Regression and Classification
What regression stands for?
Continuous space and metrics
Linear regression
Logistic regression
Regularization
Multivariate regression
Heteroscedasticity
Regression trees
Classification metrics
Multiclass problems
Perceptron
Generalization error and overfitting
Summary
Chapter 6: Working with Unstructured Data
Nested data
Other serialization formats
Hive and Impala
Sessionization
Working with traits
Working with pattern matching
Other uses of unstructured data
Probabilistic structures
Projections
Summary
Chapter 7: Working with Graph Algorithms
A quick introduction to graphs
SBT
Graph for Scala
GraphX
Summary
Chapter 8: Integrating Scala with R and Python
Integrating with R
Integrating with Python
Summary
Chapter 9: NLP in Scala
Text analysis pipeline
MLlib algorithms in Spark
Segmentation, annotation, and chunking
POS tagging
Using word2vec to find word relationships
Summary
Chapter 10: Advanced Model Monitoring
System monitoring
Summary

Book Details

ISBN 139781785880889
Paperback310 pages
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