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Implementing Machine Learning Workflow with Weka

via Pluralsight


In this course, you will learn how you can develop your machine learning workflow using Weka, an open-source machine learning software for data preparation, machine learning, and predictive model deployment.

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.

  • Course Overview
  • Implementing Regression Models
  • Implementing Classification Models
  • Implementing Clustering Models

Taught by

Janani Ravi

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