Data collection, processing, analysis, and more About This Book *
Your entry ticket to the world of data science with the stability
and power of Java * Explore, analyse, and visualize your data
effectively using easy-to-follow examples * A highly practical
course covering a broad set of topics - from the basics of Machine
Learning to Deep Learning and Big Data frameworks. Who This Book Is
For This course is meant for Java developers who are comfortable
developing applications in Java, and now want to enter the world of
data science or wish to build intelligent applications. Aspiring
data scientists with some understanding of the Java programming
language will also find this book to be very helpful. If you are
willing to build efficient data science applications and bring them
in the enterprise environment without changing your existing Java
stack, this book is for you! What You Will Learn * Understand the
key concepts of data science * Explore the data science ecosystem
available in Java * Work with the Java APIs and techniques used to
perform efficient data analysis * Find out how to approach
different machine learning problems with Java * Process
unstructured information such as natural language text or images,
and create your own search * Learn how to build deep neural
networks with DeepLearning4j * Build data science applications that
scale and process large amounts of data * Deploy data science
models to production and evaluate their performance In Detail 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 Style and approach This course
follows a tutorial approach, providing examples of each of the
concepts covered. With a step-by-step instructional style, this
book covers various facets of data science and will get you up and
running quickly.
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