Java: Data Science Made Easy

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...