Machine Learning with Scala Quick Start Guide电子书

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Preface

Who this book is for

What this book covers

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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

Technical requirements

An overview of regression analysis

Learning

Inferencing

Regression analysis algorithms

Performance metrics

Learning regression analysis through examples

Description of the dataset

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

Summary

Scala for Learning Classification

Technical requirements

Overview of classification

Developing predictive models for churn

Description of the dataset

Exploratory analysis and feature engineering

LR for churn prediction

NB for churn prediction

SVM for churn prediction

Summary

Scala for Tree-Based Ensemble Techniques

Technical requirements

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?

Summary

Scala for Dimensionality Reduction and Clustering

Technical requirements

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

Description of the dataset

Preparing the programming environment

Clustering geographic ethnicity

Training the k-means algorithm

Dimensionality reduction

Principal component analysis with Spark ML

Determining the optimal number of clusters

The elbow method

The silhouette analysis

Summary

Scala for Recommender System

Technical requirements

Overview of recommendation systems

Types of recommender systems

Similarity-based recommender systems

Content-based filtering approaches

Collaborative filtering approaches

The utility matrix

Model-based book recommendation system

Matrix factorization

Exploratory analysis

Prepare training and test rating data

Adding new user ratings and making new predictions

Summary

Introduction to Deep Learning with Scala

Technical requirements

DL versus ML

DL and ANNs

ANNs and the human brain

A brief history of neural networks

How does an ANN learn?

Training a neural network

Weight and bias initialization

Activation functions

Neural network architectures

DNNs

Autoencoders

CNNs

RNNs

Generative adversarial networks (GANs)

Capsule networks

DL frameworks

Getting started with learning

Description of the dataset

Preparing the programming environment

Preprocessing

Dataset preparation

LSTM network construction

Network training

Evaluating the model

Observing the training using Deeplearning4j UI

Summary

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