Book Image

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

By : Boštjan Kaluža, Jennifer L. Reese, Krishna Choppella, Richard M. Reese, Uday Kamath
Book Image

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

By: Boštjan Kaluža, Jennifer L. Reese, Krishna Choppella, Richard M. Reese, Uday Kamath

Overview of this book

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.

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Machine Learning: End-to-End guide for Java developers
Boštjan Kaluža | Jennifer L. Reese | Krishna Choppella | Richard M. Reese | Uday Kamath
Oct, 2017 | 19 hours 19 minutes
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Table of Contents (5 chapters)
Preface
Preface
What this learning path covers
What you need for this learning path
Who this learning path is for
Reader feedback
Customer support
Free Chapter
1
1. Module 1
1. Module 1
1. Getting Started with Data Science
2. Data Acquisition
3. Data Cleaning
4. Data Visualization
5. Statistical Data Analysis Techniques
6. Machine Learning
7. Neural Networks
8. Deep Learning
9. Text Analysis
10. Visual and Audio Analysis
11. Mathematical and Parallel Techniques for Data Analysis
12. Bringing It All Together
2
2. Module 2
2. Module 2
1. Applied Machine Learning Quick Start
2. Java Libraries and Platforms for Machine Learning
3. Basic Algorithms – Classification, Regression, and Clustering
4. Customer Relationship Prediction with Ensembles
5. Affinity Analysis
6. Recommendation Engine with Apache Mahout
7. Fraud and Anomaly Detection
8. Image Recognition with Deeplearning4j
9. Activity Recognition with Mobile Phone Sensors
10. Text Mining with Mallet – Topic Modeling and Spam Detection
11. What is Next?
A. References
3
3. Module 3
3. Module 3
1. Machine Learning Review
2. Practical Approach to Real-World Supervised Learning
3. Unsupervised Machine Learning Techniques
4. Semi-Supervised and Active Learning
5. Real-Time Stream Machine Learning
6. Probabilistic Graph Modeling
7. Deep Learning
8. Text Mining and Natural Language Processing
9. Big Data Machine Learning – The Final Frontier
A. Linear Algebra
B. Probability
D. Bibliography
4
Index
Index

What you need for this learning path

Module 1:

Many of the examples in this module use Java 8 features. There are a number of Java APIs demonstrated, each of which is introduced before it is applied. An IDE is not required but is desirable.

Module 2:

To follow the examples throughout the module, you'll need a personal computer with the JDK installed. All the examples and source code that you can download assume Eclipse IDE with support for Maven, a dependency management and build automation tool; and Git, a version control system. Examples in the chapters rely on various libraries, including Weka, deeplearning4j, Mallet, and Apache Mahout. Instructions on how to get and install the libraries are provided in the chapter where the library will be first used.

The module has a dedicated web site, http://machine-learning-in-java.com, where you can find all the example code, errata, and additional materials that will help you to get started.

Module 3:

This book assumes you have some experience of programming in Java and a basic understanding of machine learning concepts. If that doesn't apply to you, but you are curious nonetheless and self-motivated, fret not, and read on! For those who do have some background, it means that you are familiar with simple statistical analysis of data and concepts involved in supervised and unsupervised learning. Those who may not have the requisite math or must poke the far reaches of their memory to shake loose the odd formula or funny symbol, do not be disheartened. If you are the sort that loves a challenge, the short primer in the appendices may be all you need to kick-start your engines—a bit of tenacity will see you through the rest! For those who have never been introduced to machine learning, the first chapter was equally written for you as for those needing a refresher—it is your starter-kit to jump in feet first and find out what it's all about. You can augment your basics with any number of online resources. Finally, for those innocent of Java, here's a secret: many of the tools featured in the book have powerful GUIs. Some include wizard-like interfaces, making them quite easy to use, and do not require any knowledge of Java. So if you are new to Java, just skip the examples that need coding and learn to use the GUI-based tools instead!

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