Building Machine Learning Solutions with Java

Paths

Building Machine Learning Solutions with Java

Authors: Federico Mestrone, Nicolae Caprarescu, Janani Ravi

Machine learning has transformed our world, initiating new approaches to software development and applications. As a Java Developer to stay ahead of the upcoming changes we can... Read more

  • Exploring Java Machine Learning Environments
  • Implementing Machine Learning workflow with RapidMiner
  • Implementing Machine Learning workflow with Weka
  • Preparing Data for Machine Learning with Java

Pre-requisites

  • Java Fundamentals
  • Data Analytics Literacy
  • Statistics
  • Machine Learning Literacy

Beginner

Get started with Machine learning using Java. This section will cover topics like Data import and manipulation.

Preparing Data for Machine Learning with Java

by Federico Mestrone

Oct 27, 2020 / 2h 2m

2h 2m

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Description

Machine learning algorithms require that data is formatted and presented in very specific ways. In this course, Preparing Data for Machine Learning with Java, you’ll learn to use the standard Java API to make data ready for ML libraries. First, you’ll explore various options to read files into Java objects and data structures. Next, you’ll discover how to scrape the web for data you could use in your ML models. Finally, you’ll learn how to perform transformation both in vanilla Java and at scale with the Beam SDK. When you’re finished with this course, you’ll have the skills and knowledge of data gathering needed to digitize various sources into Java data structures.

Table of contents
  1. Course Overview
  2. Ingesting Data from Files in Various Formats
  3. Automating Data Collection and Scheduling
  4. Data Cleaning Using Regex and Formatter
  5. Data Transformation
  6. Data Preparation at Scale

Intermediate

This section will help you choose the appropriate tool for your machine learning solution.

Exploring Java Machine Learning Environments

by Nicolae Caprarescu

Feb 17, 2021 / 1h 35m

1h 35m

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Description

Choosing the right tool for a machine learning problem among the myriad options is not easy. In this course, Exploring Java Machine Learning Environments, you’ll learn to assess, identify, and use the right tool for the job. First, you’ll explore several characteristics of the available tools for machine learning in Java. Next, you’ll discover the pros and cons of each tool depending on multiple scenarios. Finally, you’ll learn how to get started with each of the tools, consuming data, training a model, evaluating and visualizing the performance in different environments and at different scales. When you’re finished with this course, you’ll have the skills and knowledge of the Machine Learning Java Environment needed to effectively implement industry-grade pipelines.

Table of contents
  1. Course Overview
  2. Understanding the Java Machine Learning Ecosystem
  3. Implementing a Machine Learning Workflow with Weka
  4. Implementing a Machine Learning Workflow with DL4J
  5. Implementing a Machine Learning Workflow with Spark MLlib

Advanced

In the section you will learn to develop your machine learning workflow using Rapid Miner studio and Weka. These data science platforms are popular for data preparation , machine learning and predictive model deployment.

Implementing Machine Learning Workflow with Weka

by Janani Ravi

Dec 11, 2020 / 2h 1m

2h 1m

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Description

Weka is a tried and tested open-source machine learning software for building all components of a machine learning workflow. In this course, Implementing Machine Learning Workflow with Weka, you will learn terminal applications as well as a Java API to train models. Weka is commonly used for teaching, research, and industrial applications.

First, you will get started with an Apache Maven project and set up your Java development environment with all of the dependencies that you need for building Weka applications. Next, you will explore building and evaluating classification models in Weka.

Finally, you will implement unsupervised learning techniques in Weka and perform clustering using the k-means clustering algorithm, hierarchical clustering as well as expectation-maximization clustering.

When you are finished with this course, you will have the knowledge and skills to build supervised and unsupervised machine learning models using the Weka Java library.

Table of contents
  1. Course Overview
  2. Implementing Regression Models
  3. Implementing Classification Models
  4. Implementing Clustering Models

Implementing Machine Learning Workflow with RapidMiner

by Janani Ravi

Dec 8, 2020 / 2h 23m

2h 23m

Start Course
Description

RapidMiner Studio provides an integrated development environment for data visualization, data preparation, machine learning, and deployment. In this course, Implementing Machine Learning Workflow with RapidMiner, you will get an overview of how you can use drag-n-drop operators to build and train machine learning models.

First, you will get introduced to RapidMiner studio, which is a no-code technology to develop your machine learning workflow. You will perform exploratory data analysis using RapidMiner, build linear regression models, evaluate models using cross-validation, and perform feature selection and normalization of input data, without writing a single line of code.

Next, you will explore a native Java library for traditional machine learning models. The Java Statistical Analysis Tool, or JSAT library, is a pure Java library that allows you to train regression, classification, and clustering models. You will use JSAT to perform linear regression, perform classification using logistic regression and decision trees, perform clustering using k-means clustering, and deploy your model using the SpringBoot framework in a limited production environment.

Finally, you will see how you can use the Deep Java Library, or DJL, to train neural network models in Java. DJL provides a native Java API and can run your training on multiple backends such as Apache MXNet, TensorFlow, and PyTorch. You will also leverage transfer learning and use pre-trained models for image classification, image segmentation, and natural language processing.

When you are finished with this course, you will be able to use no-code technologies and native Java libraries to build and train machine learning models.

Table of contents
  1. Course Overview
  2. Implementing Machine Learning Models with RapidMiner Studio
  3. Using JSAT to Implement Machine Learning Models
  4. Using DJL to Implement Machine Learning Models
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