Machine Learning: End-to-End guide for Java developers

Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming
Preview in Mapt

Machine Learning: End-to-End guide for Java developers

Richard M. Reese et al.

1 customer reviews
Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming

Quick links: > What will you learn?> Table of content> Product reviews

Mapt Subscription
FREE
$29.99/m after trial
eBook
$53.20
RRP $75.99
Save 29%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$53.20
$29.99 p/m after trial
RRP $75.99
Subscription
eBook
Start 14 Day Trial

Frequently bought together


Machine Learning: End-to-End guide for Java developers Book Cover
Machine Learning: End-to-End guide for Java developers
$ 75.99
$ 53.20
Understanding Software Book Cover
Understanding Software
$ 23.99
$ 16.80
Buy 2 for $34.30
Save $65.68
Add to Cart

Book Details

ISBN 139781788622219
Paperback1159 pages

Book Description

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.

The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:

  • Java for Data Science
  • Machine Learning in Java
  • Mastering Java Machine Learning

On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.

Table of Contents

Chapter 1: Getting Started with Data Science
Problems solved using data science
Understanding the data science problem -  solving approach
Acquiring data for an application
The importance and process of cleaning data
Visualizing data to enhance understanding
The use of statistical methods in data science
Machine learning applied to data science
Using neural networks in data science
Deep learning approaches
Performing text analysis
Visual and audio analysis
Improving application performance using parallel techniques
Assembling the pieces
Summary
Chapter 2: Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
Chapter 3: Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
Chapter 4: Data Visualization
Understanding plots and graphs
Creating index charts
Creating bar charts
Creating stacked graphs
Creating pie charts
Creating scatter charts
Creating histograms
Creating donut charts
Creating bubble charts
Summary
Chapter 5: Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
Chapter 6: Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
Chapter 7: Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
Chapter 8: Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
Chapter 9: Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
Chapter 10: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 11: Mathematical and Parallel Techniques for Data Analysis
Implementing basic matrix operations
Using map-reduce
Various mathematical libraries
Using OpenCL
Using Aparapi
Using Java 8 streams
Summary
Chapter 12: Bringing It All Together
Defining the purpose and scope of our application
Understanding the application's architecture
Data acquisition using Twitter
Understanding the TweetHandler class
Other optional enhancements
Summary
Chapter 13: Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
Chapter 14: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 15: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 16: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 17: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 18: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 19: Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
Chapter 20: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 21: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 22: Text Mining with Mallet – Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
Chapter 23: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
Chapter 24: Machine Learning Review
Machine learning – history and definition
What is not machine learning?
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
Machine learning – tools and datasets
Summary
Chapter 25: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data transformation and preprocessing
Feature relevance analysis and dimensionality reduction
Model building
Model assessment, evaluation, and comparisons
Case Study – Horse Colic Classification
Summary
References
Chapter 26: Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Clustering
Outlier or anomaly detection
Real-world case study
Summary
References
Chapter 27: Semi-Supervised and Active Learning
Semi-supervised learning
Active learning
Case study in active learning
Summary
References
Chapter 28: Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Concept drift and drift detection
Incremental supervised learning
Incremental unsupervised learning using clustering
Unsupervised learning using outlier detection
Case study in stream learning
Summary
References
Chapter 29: Probabilistic Graph Modeling
Probability revisited
Graph concepts
Bayesian networks
Markov networks and conditional random fields
Specialized networks
Tools and usage
Case study
Summary
References
Chapter 30: Deep Learning
Multi-layer feed-forward neural network
Limitations of neural networks
Deep learning
Case study
Summary
References
Chapter 31: Text Mining and Natural Language Processing
NLP, subfields, and tasks
Issues with mining unstructured data
Text processing components and transformations
Topics in text mining
Tools and usage
Summary
References
Chapter 32: Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
Batch Big Data Machine Learning
Case study

What You Will Learn

  • Understand key data analysis techniques centered around machine learning
  • Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more
  • Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them
  • Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition
  • Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models
  • Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more

Authors

Table of Contents

Chapter 1: Getting Started with Data Science
Problems solved using data science
Understanding the data science problem -  solving approach
Acquiring data for an application
The importance and process of cleaning data
Visualizing data to enhance understanding
The use of statistical methods in data science
Machine learning applied to data science
Using neural networks in data science
Deep learning approaches
Performing text analysis
Visual and audio analysis
Improving application performance using parallel techniques
Assembling the pieces
Summary
Chapter 2: Data Acquisition
Understanding the data formats used in data science applications
Data acquisition techniques
Summary
Chapter 3: Data Cleaning
Handling data formats
The nitty gritty of cleaning text
Cleaning images
Summary
Chapter 4: Data Visualization
Understanding plots and graphs
Creating index charts
Creating bar charts
Creating stacked graphs
Creating pie charts
Creating scatter charts
Creating histograms
Creating donut charts
Creating bubble charts
Summary
Chapter 5: Statistical Data Analysis Techniques
Working with mean, mode, and median
Standard deviation
Sample size determination
Hypothesis testing
Regression analysis
Summary
Chapter 6: Machine Learning
Supervised learning techniques
Unsupervised machine learning
Reinforcement learning
Summary
Chapter 7: Neural Networks
Training a neural network
Understanding static neural networks
Understanding dynamic neural networks
Additional network architectures and algorithms
Summary
Chapter 8: Deep Learning
Deeplearning4j architecture
Deep learning and regression analysis
Restricted Boltzmann Machines
Deep autoencoders
Convolutional networks
Recurrent Neural Networks
Summary
Chapter 9: Text Analysis
Implementing named entity recognition
Classifying text
Understanding tagging and POS
Extracting relationships from sentences
Sentiment analysis
Summary
Chapter 10: Visual and Audio Analysis
Text-to-speech
Understanding speech recognition
Extracting text from an image
Identifying faces
Classifying visual data
Summary
Chapter 11: Mathematical and Parallel Techniques for Data Analysis
Implementing basic matrix operations
Using map-reduce
Various mathematical libraries
Using OpenCL
Using Aparapi
Using Java 8 streams
Summary
Chapter 12: Bringing It All Together
Defining the purpose and scope of our application
Understanding the application's architecture
Data acquisition using Twitter
Understanding the TweetHandler class
Other optional enhancements
Summary
Chapter 13: Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
Chapter 14: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 15: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 16: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 17: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 18: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 19: Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
Chapter 20: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 21: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 22: Text Mining with Mallet – Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
Chapter 23: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
Chapter 24: Machine Learning Review
Machine learning – history and definition
What is not machine learning?
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
Machine learning – tools and datasets
Summary
Chapter 25: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data transformation and preprocessing
Feature relevance analysis and dimensionality reduction
Model building
Model assessment, evaluation, and comparisons
Case Study – Horse Colic Classification
Summary
References
Chapter 26: Unsupervised Machine Learning Techniques
Issues in common with supervised learning
Issues specific to unsupervised learning
Feature analysis and dimensionality reduction
Clustering
Outlier or anomaly detection
Real-world case study
Summary
References
Chapter 27: Semi-Supervised and Active Learning
Semi-supervised learning
Active learning
Case study in active learning
Summary
References
Chapter 28: Real-Time Stream Machine Learning
Assumptions and mathematical notations
Basic stream processing and computational techniques
Concept drift and drift detection
Incremental supervised learning
Incremental unsupervised learning using clustering
Unsupervised learning using outlier detection
Case study in stream learning
Summary
References
Chapter 29: Probabilistic Graph Modeling
Probability revisited
Graph concepts
Bayesian networks
Markov networks and conditional random fields
Specialized networks
Tools and usage
Case study
Summary
References
Chapter 30: Deep Learning
Multi-layer feed-forward neural network
Limitations of neural networks
Deep learning
Case study
Summary
References
Chapter 31: Text Mining and Natural Language Processing
NLP, subfields, and tasks
Issues with mining unstructured data
Text processing components and transformations
Topics in text mining
Tools and usage
Summary
References
Chapter 32: Big Data Machine Learning – The Final Frontier
What are the characteristics of Big Data?
Big Data Machine Learning
Batch Big Data Machine Learning
Case study

Book Details

ISBN 139781788622219
Paperback1159 pages
Read More
From 1 reviews

Read More Reviews

Recommended for You

Understanding Software Book Cover
Understanding Software
$ 23.99
$ 16.80
Kotlin for Android & Java Developers: Clean Code on Android [Video] Book Cover
Kotlin for Android & Java Developers: Clean Code on Android [Video]
$ 191.99
$ 163.20
Ultimate Java Development and Certification Guide [Video] Book Cover
Ultimate Java Development and Certification Guide [Video]
$ 29.99
$ 25.50
Mastering Machine Learning with MATLAB [Video] Book Cover
Mastering Machine Learning with MATLAB [Video]
$ 124.99
$ 106.25
C++ Development Tutorial Series - The Complete Coding Guide [Video] Book Cover
C++ Development Tutorial Series - The Complete Coding Guide [Video]
$ 99.99
$ 85.00
Augmented Reality for JavaScript Developers [Video] Book Cover
Augmented Reality for JavaScript Developers [Video]
$ 124.99
$ 106.25