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Machine Learning in Java电子书

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75人正在读 | 0人评论 9.8

作       者:Boštjan Kaluža

出  版  社:Packt Publishing

出版时间:2016-04-01

字       数:222.8万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries About This Book Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications Packed with practical advice and tips to help you get to grips with applied machine learning Who This Book Is For If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary. What You Will Learn Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve Learn how to implement classification, regression, and clustering Develop a sustainable strategy for customer retention by predicting likely churn candidates Build a scalable recommendation engine with Apache Mahout Apply machine learning to fraud, anomaly, and outlier detection Experiment with deep learning concepts, algorithms, and the toolbox for deep learning Write your own activity recognition model for eHealth applications using mobile sensors In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will explore related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. Style and approach This is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with Java libraries using clear and practical examples. You will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
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Machine Learning in Java

Table of Contents

Machine Learning in Java

Credits

About the Author

About the Reviewers

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

Supporting materials

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Applied Machine Learning Quick Start

Machine learning and data science

What kind of problems can machine learning solve?

Applied machine learning workflow

Data and problem definition

Measurement scales

Data collection

Find or observe data

Generate data

Sampling traps

Data pre-processing

Data cleaning

Fill missing values

Remove outliers

Data transformation

Data reduction

Unsupervised learning

Find similar items

Euclidean distances

Non-Euclidean distances

The curse of dimensionality

Clustering

Supervised learning

Classification

Decision tree learning

Probabilistic classifiers

Kernel methods

Artificial neural networks

Ensemble learning

Evaluating classification

Precision and recall

Roc curves

Regression

Linear regression

Evaluating regression

Mean squared error

Mean absolute error

Correlation coefficient

Generalization and evaluation

Underfitting and overfitting

Train and test sets

Cross-validation

Leave-one-out validation

Stratification

Summary

2. Java Libraries and Platforms for Machine Learning

The need for Java

Machine learning libraries

Weka

Java machine learning

Apache Mahout

Apache Spark

Deeplearning4j

MALLET

Comparing libraries

Building a machine learning application

Traditional machine learning architecture

Dealing with big data

Big data application architecture

Summary

3. Basic Algorithms – Classification, Regression, and Clustering

Before you start

Classification

Data

Loading data

Feature selection

Learning algorithms

Classify new data

Evaluation and prediction error metrics

Confusion matrix

Choosing a classification algorithm

Regression

Loading the data

Analyzing attributes

Building and evaluating regression model

Linear regression

Regression trees

Tips to avoid common regression problems

Clustering

Clustering algorithms

Evaluation

Summary

4. Customer Relationship Prediction with Ensembles

Customer relationship database

Challenge

Dataset

Evaluation

Basic naive Bayes classifier baseline

Getting the data

Loading the data

Basic modeling

Evaluating models

Implementing naive Bayes baseline

Advanced modeling with ensembles

Before we start

Data pre-processing

Attribute selection

Model selection

Performance evaluation

Summary

5. Affinity Analysis

Market basket analysis

Affinity analysis

Association rule learning

Basic concepts

Database of transactions

Itemset and rule

Support

Confidence

Apriori algorithm

FP-growth algorithm

The supermarket dataset

Discover patterns

Apriori

FP-growth

Other applications in various areas

Medical diagnosis

Protein sequences

Census data

Customer relationship management

IT Operations Analytics

Summary

6. Recommendation Engine with Apache Mahout

Basic concepts

Key concepts

User-based and item-based analysis

Approaches to calculate similarity

Collaborative filtering

Content-based filtering

Hybrid approach

Exploitation versus exploration

Getting Apache Mahout

Configuring Mahout in Eclipse with the Maven plugin

Building a recommendation engine

Book ratings dataset

Loading the data

Loading data from file

Loading data from database

In-memory database

Collaborative filtering

User-based filtering

Item-based filtering

Adding custom rules to recommendations

Evaluation

Online learning engine

Content-based filtering

Summary

7. Fraud and Anomaly Detection

Suspicious and anomalous behavior detection

Unknown-unknowns

Suspicious pattern detection

Anomalous pattern detection

Analysis types

Pattern analysis

Transaction analysis

Plan recognition

Fraud detection of insurance claims

Dataset

Modeling suspicious patterns

Vanilla approach

Dataset rebalancing

Anomaly detection in website traffic

Dataset

Anomaly detection in time series data

Histogram-based anomaly detection

Loading the data

Creating histograms

Density based k-nearest neighbors

Summary

8. Image Recognition with Deeplearning4j

Introducing image recognition

Neural networks

Perceptron

Feedforward neural networks

Autoencoder

Restricted Boltzmann machine

Deep convolutional networks

Image classification

Deeplearning4j

Getting DL4J

MNIST dataset

Loading the data

Building models

Building a single-layer regression model

Building a deep belief network

Build a Multilayer Convolutional Network

Summary

9. Activity Recognition with Mobile Phone Sensors

Introducing activity recognition

Mobile phone sensors

Activity recognition pipeline

The plan

Collecting data from a mobile phone

Installing Android Studio

Loading the data collector

Feature extraction

Collecting training data

Building a classifier

Reducing spurious transitions

Plugging the classifier into a mobile app

Summary

10. Text Mining with Mallet – Topic Modeling and Spam Detection

Introducing text mining

Topic modeling

Text classification

Installing Mallet

Working with text data

Importing data

Importing from directory

Importing from file

Pre-processing text data

Topic modeling for BBC news

BBC dataset

Modeling

Evaluating a model

Reusing a model

Saving a model

Restoring a model

E-mail spam detection

E-mail spam dataset

Feature generation

Training and testing

Model performance

Summary

11. What is Next?

Machine learning in real life

Noisy data

Class unbalance

Feature selection is hard

Model chaining

Importance of evaluation

Getting models into production

Model maintenance

Standards and markup languages

CRISP-DM

SEMMA methodology

Predictive Model Markup Language

Machine learning in the cloud

Machine learning as a service

Web resources and competitions

Datasets

Online courses

Competitions

Websites and blogs

Venues and conferences

Summary

A. References

Index

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