Mastering Scala Machine Learning电子书

Mastering Scala Machine Learning

Table of Contents

Mastering Scala Machine Learning

Credits

About the Author

Acknowlegement

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Exploratory Data Analysis

Getting started with Scala

Distinct values of a categorical field

Summarization of a numeric field

Grepping across multiple fields

Basic, stratified, and consistent sampling

Working with Scala and Spark Notebooks

Basic correlations

Summary

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

Data ingest

Data transformation layer

Data analytics and machine learning

UI component

Actions engine

Correlation engine

Monitoring

Optimization and interactivity

Feedback loops

Summary

3. Working with Spark and MLlib

Setting up Spark

Understanding Spark architecture

Task scheduling

Spark components

MQTT, ZeroMQ, Flume, and Kafka

HDFS, Cassandra, S3, and Tachyon

Mesos, YARN, and Standalone

Applications

Word count

Streaming word count

Spark SQL and DataFrame

ML libraries

SparkR

Graph algorithms – GraphX and GraphFrames

Spark performance tuning

Running Hadoop HDFS

Summary

4. Supervised and Unsupervised Learning

Records and supervised learning

Iris dataset

Labeled point

SVMWithSGD

Logistic regression

Decision tree

Bagging and boosting – ensemble learning methods

Unsupervised learning

Problem dimensionality

Summary

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

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

7. Working with Graph Algorithms

A quick introduction to graphs

SBT

Graph for Scala

Adding nodes and edges

Graph constraints

JSON

GraphX

Who is getting e-mails?

Connected components

Triangle counting

Strongly connected components

PageRank

SVD++

Summary

8. Integrating Scala with R and Python

Integrating with R

Setting up R and SparkR

Linux

Mac OS

Windows

Running SparkR via scripts

Running Spark via R's command line

DataFrames

Linear models

Generalized linear model

Reading JSON files in SparkR

Writing Parquet files in SparkR

Invoking Scala from R

Using Rserve

Integrating with Python

Setting up Python

PySpark

Calling Python from Java/Scala

Using sys.process._

Spark pipe

Jython and JSR 223

Summary

9. NLP in Scala

Text analysis pipeline

Simple text analysis

MLlib algorithms in Spark

TF-IDF

LDA

Segmentation, annotation, and chunking

POS tagging

Using word2vec to find word relationships

A Porter Stemmer implementation of the code

Summary

10. Advanced Model Monitoring

System monitoring

Process monitoring

Model monitoring

Performance over time

Criteria for model retiring

A/B testing

Summary

Index