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.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  2mins
• Implementing Regression Models
  51mins
• Implementing Classification Models
  29mins
• Implementing Clustering Models
  39mins