更新时间：2021-07-09 21:08:48
coverpage
Title Page
Copyright
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
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
Getting Up and Running with Spark
Installing and setting up Spark locally
Spark clusters
The Spark programming model
SparkContext and SparkConf
SparkSession
The Spark shell
Resilient Distributed Datasets
Creating RDDs
Spark operations
Caching RDDs
Broadcast variables and accumulators
SchemaRDD
Spark data frame
The first step to a Spark program in Scala
The first step to a Spark program in Java
The first step to a Spark program in Python
The first step to a Spark program in R
SparkR DataFrames
Getting Spark running on Amazon EC2
Launching an EC2 Spark cluster
Configuring and running Spark on Amazon Elastic Map Reduce
UI in Spark
Supported machine learning algorithms by Spark
Benefits of using Spark ML as compared to existing libraries
Spark Cluster on Google Compute Engine - DataProc
Hadoop and Spark Versions
Creating a Cluster
Submitting a Job
Summary
Math for Machine Learning
Linear algebra
Setting up the Scala environment in Intellij
Setting up the Scala environment on the Command Line
Fields
Real numbers
Complex numbers
Vectors
Vector spaces
Vector types
Vectors in Breeze
Vectors in Spark
Vector operations
Hyperplanes
Vectors in machine learning
Matrix
Types of matrices
Matrix in Spark
Distributed matrix in Spark
Matrix operations
Determinant
Eigenvalues and eigenvectors
Singular value decomposition
Matrices in machine learning
Functions
Function types
Functional composition
Hypothesis
Gradient descent
Prior likelihood and posterior
Calculus
Differential calculus
Integral calculus
Lagranges multipliers
Plotting
Designing a Machine Learning System
What is Machine Learning?
Introducing MovieStream
Business use cases for a machine learning system
Personalization
Targeted marketing and customer segmentation
Predictive modeling and analytics
Types of machine learning models
The components of a data-driven machine learning system
Data ingestion and storage
Data cleansing and transformation
Model training and testing loop
Model deployment and integration
Model monitoring and feedback
