When you enroll through our links, we may earn a small commission—at no extra cost to you. This helps keep our platform free and inspires us to add more value.
Implementing Machine Learning Workflow with Weka
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.
This Course Includes
pluralsight
0 (0 reviews )
2 hour 2 minutes 
english
Online - Self Paced
core courses
pluralsight
About Implementing Machine Learning Workflow with Weka
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.
What You Will Learn?
- Course Overview : 2mins.
- Implementing Regression Models : 51mins.
- Implementing Classification Models : 29mins.
- Implementing Clustering Models : 39mins.