Implementing Machine Learning Workflow with Weka

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Janani Ravi

2:02:09

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1. Course Overview

1. Course Overview.mp4

02:05

02. Implementing Regression Models

01. Version Check.mp4

00:15

02. Prerequisites and Course Outline.mp4

02:15

03. Introducing Weka.mp4

02:24

04. Demo - Environment and Project Setup.mp4

04:07

05. Demo - Exploring the Weka Workbench.mp4

05:07

06. Demo - Loading and Exploring the Dataset.mp4

03:55

07. Demo - Training and Evaluating a Regression Model.mp4

04:59

08. Demo - Training and Evaluating a Multiple Regression Model.mp4

04:56

09. Demo - Feature Selection and Ranking.mp4

06:35

10. Demo - Processing and Saving Processed Data.mp4

04:35

11. Demo - Evaluating a Model Using Cross Validation.mp4

01:59

12. Demo - Regression Using Support Vector Machines and Multilayer Perceptrons.mp4

03:31

13. Demo - Serializing and Visualizing a Decision Tree Model.mp4

06:25

3. Implementing Classification Models

1. Demo - Feature Selection and Data Processing.mp4

06:13

2. Demo - Building and Evaluating a Classification Model.mp4

06:12

3. Demo - Building and Visualizing a Decision Tree Model.mp4

03:41

4. Demo - Encoding Text Data in Numeric Form.mp4

05:42

5. Demo - Performing Classification on Text Data.mp4

07:20

4. Implementing Clustering Models

1. Demo - Normalizing and Visualizing Data.mp4

06:09

2. Demo - Performing K-means Clustering.mp4

03:45

3. Demo - Visualizing Cluster Assignments.mp4

05:55

4. Demo - Exploring and Visualizing Data.mp4

03:20

5. Demo - Performing Hierarchical Clustering.mp4

04:54

6. Demo - Performing EM Clustering.mp4

03:38

7. Demo - Serializing Trained Model Parameters.mp4

03:25

8. Demo - Deploying a Model Using SpringBoot.mp4

07:04

9. Summary and Further Study.mp4

01:43

Description

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.

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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Machine Learning

Janani Ravi

Instructor's Courses

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 high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.