更新时间：2021-06-24 14:32:26
coverpage
Title Page
Copyright and Credits
Machine Learning with Scala Quick Start Guide
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Conventions used
Get in touch
Reviews
Introduction to Machine Learning with Scala
Technical requirements
Overview of ML
Working principles of a learning algorithm
General machine learning rule of thumb
General issues in machine learning models
ML tasks
Supervised learning
Unsupervised learning
Reinforcement learning
Summarizing learning types with applications
Overview of Scala
ML libraries in Scala
Spark MLlib and ML
ScalNet and DynaML
ScalaNLP Vegas and Breeze
Getting started learning
Description of the dataset
Configuring the programming environment
Getting started with Apache Spark
Reading the training dataset
Preprocessing and feature engineering
Preparing training data and training a classifier
Evaluating the model
Summary
Scala for Regression Analysis
An overview of regression analysis
Learning
Inferencing
Regression analysis algorithms
Performance metrics
Learning regression analysis through examples
Exploratory analysis of the dataset
Feature engineering and data preparation
Linear regression
Generalized linear regression (GLR)
Hyperparameter tuning and cross-validation
Hyperparameter tuning
Cross-validation
Tuning and cross-validation in Spark ML
Scala for Learning Classification
Overview of classification
Developing predictive models for churn
Exploratory analysis and feature engineering
LR for churn prediction
NB for churn prediction
SVM for churn prediction
Scala for Tree-Based Ensemble Techniques
Decision trees and tree ensembles
Decision trees for supervised learning
Decision trees for classification
Decision trees for regression
Gradient boosted trees for supervised learning
Gradient boosted trees for classification
GBTs for regression
Random forest for supervised learning
Random forest for classification
Random forest for regression
What's next?
Scala for Dimensionality Reduction and Clustering
Overview of unsupervised learning
Clustering analysis
Clustering analysis algorithms
K-means for clustering analysis
Bisecting k-means
Gaussian mixture model
Other clustering analysis algorithms
Clustering analysis through examples
Preparing the programming environment
Clustering geographic ethnicity
