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Working in a hands-on learning environment, led by our Java for Data Science expert instructor, students will learn about and explore: 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.

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Unlimited Duration

Last Updated

January 6, 2021

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Certification

Course Description

Java 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 course 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 course by talking about the ways to deploy the model and evaluate it in production settings.

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Course Curriculum

    • Data Science Using Java 00:00:00
    • Data science 00:00:00
    • Data science process models 00:00:00
    • Data science in Java 00:00:00
    • Data Processing Toolbox 00:00:00
    • Standard Java library 00:00:00
    • Extensions to the standard library 00:00:00
    • Accessing data 00:00:00
    • Search engine – preparing data 00:00:00
    • Exploratory Data Analysis 00:00:00
    • Exploratory data analysis in Java 00:00:00
    • Interactive Exploratory Data Analysis in Java 00:00:00
    • Supervised Learning – Classification and Regression 00:00:00
    • Classification 00:00:00
    • Case study – page prediction 00:00:00
    • Regression 00:00:00
    • Case study – hardware performance 00:00:00
    • Unsupervised Learning – Clustering and Dimensionality Reduction 00:00:00
    • Dimensionality reduction 00:00:00
    • Cluster analysis 00:00:00
    • Working with Text – Natural Language Processing and Information Retrieval 00:00:00
    • Natural Language Processing and information retrieval 00:00:00
    • Machine learning for texts 00:00:00
    • Extreme Gradient Boosting 00:00:00
    • Gradient Boosting Machines and XGBoost 00:00:00
    • XGBoost in practice 00:00:00
    • Deep Learning with DeepLearning4J 00:00:00
    • Neural Networks and DeepLearning4J 00:00:00
    • Deep learning for cats versus dogs 00:00:00
    • Scaling Data Science 00:00:00
    • Apache Hadoop 00:00:00
    • Apache Spark 00:00:00
    • Link prediction 00:00:00
    • Summary 00:00:00
    • 10Deploying Data Science Models 00:00:00
    • Deploying Data Science Models 00:00:00
    • Microservices 00:00:00
    • Online evaluation 00:00:00

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