Analytics
Exploratory Data Analysis
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.
Pluralsight Teams₹4,166.75 per user/month, billed annually
₹50,004
CompleteBilled Monthly
₹2,370/mo
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Course Insight
Suitable for intermediate learners. Works well as a continuation after mastering Data Science fundamentals. It bridges the gap toward advanced, production-level engineering.
Intermediate FriendlySelf-Paced LearningProject-Based
SKILLS TO
MASTER
ModelingTrending
Predictive Machine LearningSQL Querying
Relational Data ManagementCareers:Backend Developer, Software Engineer, API Developer.
💡This course fits perfectly into our comprehensiveData Science Learning Path. Explore the ecosystem to see how it compares to other foundational skills.
Quick Facts
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What You’ll Learn
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.
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Outcomes
- Course Overview : 2mins.
- Implementing Regression Models : 51mins.
- Implementing Classification Models : 29mins.
- Implementing Clustering Models : 39mins.
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