- eBook:Mastering Java for Data Science: Analytics and more for production-ready applications
- Author:Alexey Grigorev
- Data:April 27, 2017
- Pages:364 pages
- Format:PDF, ePUB
- An overview of modern Data Science and Machine Learning libraries available in Java
- Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.
- Easy-to-follow illustrations and the running example of building a search engine.
Book DescriptionJava is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.
Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.
This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.
Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
What you will learn
- Get a solid understanding of the data processing toolbox available in Java
- Explore the data science ecosystem available in Java
Chapter 2: Data Processing Toolbox
Chapter 3: Exploratory Data Analysis
Chapter 4: Supervised Learning - Classification and Regression
Chapter 5: Unsupervised Learning - Clustering and Dimensionality Reduction
Chapter 6: Working with Text - Natural Language Processing and Information Retrieval
Chapter 7: Extreme Gradient Boosting
Chapter 8: Deep Learning with DeepLearning4J
Chapter 9: Scaling Data Science
Chapter 10: Deploying Data Science Models