Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Java: Data Science Made Easy

Buy
Java: Data Science Made Easy, 9781788475655 (1788475658), Packt Publishing, 2017

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.

(HTML tags aren't allowed.)

Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark
Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies.

Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book...

Big Data Application Architecture Q&A: A Problem - Solution Approach (Expert's Voice in Big Data)
Big Data Application Architecture Q&A: A Problem - Solution Approach (Expert's Voice in Big Data)

Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of...

Big Data Analytics with Hadoop 3: Build highly effective analytics solutions to gain valuable insight into your big data
Big Data Analytics with Hadoop 3: Build highly effective analytics solutions to gain valuable insight into your big data

Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3

Key Features

  • Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud
  • Integrate Hadoop with other big data tools such as R, Python, Apache...

Modern Big Data Processing with Hadoop: Expert techniques for architecting end-to-end Big Data solutions to get valuable insights
Modern Big Data Processing with Hadoop: Expert techniques for architecting end-to-end Big Data solutions to get valuable insights

A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop

Key Features

  • Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform
  • Conquer different data...
Complete Guide to Open Source Big Data Stack
Complete Guide to Open Source Big Data Stack

See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together.

In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private...

Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake
Practical Enterprise Data Lake Insights: Handle Data-Driven Challenges in an Enterprise Big Data Lake
Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues.


When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the
...
©2019 LearnIT (support@pdfchm.net) - Privacy Policy