Java data science cookbook : explore the power of MLlib, DL4j, Weka, and more / Rushdi Shams.

Format:

Book

Author/Creator:

Shams, Rushdi, author.

Language:

English

Subjects (All):

Java (Computer program language).

Physical Description:

1 online resource (366 pages)

Edition:

1st edition

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Shams Rushdi: Rushdi Shams has a Ph. D. on Application of machine learning in Natural Language Processing (NLP) problem areas from Western University, Canada. Before starting work as a machine learning and NLP specialist in the industry, he was engaged in teaching undergrad and grad courses. He has been successfully maintaining his YouTube channel named "Learn with Rushdi" for learning computer technologies.

Summary:

Recipes to help you overcome your data science hurdles using Java About This Book This book provides modern recipes in small steps to help an apprentice cook become a master chef in data science Use these recipes to obtain, clean, analyze, and learn from your data Learn how to get your data science applications to production and enterprise environments effortlessly Who This Book Is For This book is for Java developers who are familiar with the fundamentals of data science and want to improve their skills to become a pro. What You Will Learn Find out how to clean and make datasets ready so you can acquire actual insights by removing noise and outliers Develop the skills to use modern machine learning techniques to retrieve information and transform data to knowledge. retrieve information from large amount of data in text format. Familiarize yourself with cutting-edge techniques to store and search large volumes of data and retrieve information from large amounts of data in text format Develop basic skills to apply big data and deep learning technologies on large volumes of data Evolve your data visualization skills and gain valuable insights from your data Get to know a step-by-step formula to develop an industry-standard, large-scale, real-life data product Gain the skills to visualize data and interact with users through data insights In Detail If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work. Style and approach This book contains short yet very effective recipes to solve most common problems. Some recipes cater to very specific, rare pain points. The recipes cover different data sets and work very closely to real production environments

Contents:

Cover

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Obtaining and Cleaning Data

Introduction

Retrieving all filenames from hierarchical directories using Java

Getting ready

How to do it…

Retrieving all filenames from hierarchical directories using Apache Commons IO

Reading contents from text files all at once using Java 8

Reading contents from text files all at once using Apache Commons IO

Extracting PDF text using Apache Tika

Cleaning ASCII text files using Regular Expressions

Parsing Comma Separated Value (CSV) Files using Univocity

Parsing Tab Separated Value (TSV) file using Univocity

Parsing XML files using JDOM

Writing JSON files using JSON.simple

Reading JSON files using JSON.simple

How to do it …

Extracting web data from a URL using JSoup

Extracting web data from a website using Selenium Webdriver

Reading table data from a MySQL database

Chapter 2: Indexing and Searching Data

Indexing data with Apache Lucene

How it works…

Searching indexed data with Apache Lucene

Chapter 3: Analyzing Data Statistically

Generating descriptive statistics

Generating summary statistics

Generating summary statistics from multiple distributions

There's more….

Computing frequency distribution

Counting word frequency in a string

Counting word frequency in a string using Java 8

Computing simple regression

Computing ordinary least squares regression

Computing generalized least squares regression

Calculating covariance of two sets of data points

Calculating Pearson's correlation of two sets of data points

Conducting a paired t-test

Conducting a Chi-square test

Conducting the one-way ANOVA test

Conducting a Kolmogorov-Smirnov test

Chapter 4: Learning from Data - Part 1

Creating and saving an Attribute-Relation File Format (ARFF) file

Cross-validating a machine learning model

Classifying unseen test data

Classifying unseen test data with a filtered classifier

Generating linear regression models

Generating logistic regression models

Clustering data points using the KMeans algorithm

Clustering data from classes

Learning association rules from data

Selecting features/attributes using the low-level method, the filtering method, and the meta-classifier method

Chapter 5: Learning from Data - Part 2

Applying machine learning on data using Java Machine Learning (Java-ML) library

Classifying data points using the Stanford classifier

Classifying data points using Massive Online Analysis (MOA).

Getting ready

Classifying multilabeled data points using Mulan

Chapter 6: Retrieving Information from Text Data

Detecting tokens (words) using Java

Detecting sentences using Java

Detecting tokens (words) and sentences using OpenNLP

Retrieving lemma, part-of-speech, and recognizing named entities from tokens using Stanford CoreNLP

Measuring text similarity with Cosine Similarity measure using Java 8

Extracting topics from text documents using Mallet

Classifying text documents using Mallet

Classifying text documents using Weka

Chapter 7: Handling Big Data

Training an online logistic regression model using Apache Mahout

Applying an online logistic regression model using Apache Mahout

Solving simple text mining problems with Apache Spark

Clustering using KMeans algorithm with MLib

Creating a linear regression model with MLib

Classifying data points with Random Forest model using MLib

Chapter 8: Learn Deeply from Data

Creating a Word2vec neural net using Deep Learning for Java (DL4j)

There's more

Creating a Deep Belief neural net using Deep Learning for Java (DL4j)

Creating a deep autoencoder using Deep Learning for Java (DL4j)

How to do it….

How it works…

Chapter 9: Visualizing Data

Plotting a 2D sine graph

Plotting histograms

Plotting a bar chart

Plotting box plots or whisker diagrams

Plotting scatter plots

Plotting donut plots

Plotting area graphs

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed April 11, 2017).

ISBN:

9781787127654

1787127656

OCLC:

983204794