Book Image

Java: Data Science Made Easy

By : Richard M. Reese, Jennifer L. Reese, Alexey Grigorev
Book Image

Java: Data Science Made Easy

By: Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

Overview of this book

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: - Java for Data Science - Mastering Java for Data Science
Table of Contents (29 chapters)
Title Page
Credits
Preface
Free Chapter
1
Module 1
15
Module 2
26
Bibliography

Summary


In this chapter, we demonstrated many techniques for processing speech and images. This capability is becoming important, as electronic devices are increasingly embracing these communication mediums.

TTS was demonstrated using FreeTSS. This technique allows a computer to present results as speech as opposed to text. We learned how we can control the attributes of the voice used, such as its gender and age.

Recognizing speech is useful and helps bridge the human-computer interface gap. We demonstrated how CMUSphinx is used to recognize human speech. As there is often more than one way speech can be interpreted, we learned how the API can return various options. We also demonstrated how individual words are extracted, along with the relative confidence that the right word was identified.

Image processing is a critical aspect of many applications. We started our discussion of image processing by use Tess4J to extract text from an image. This process is sometimes referred to as OCR. We...