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.
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.
Table of contents
- Version Check 0m
- Prerequisites and Course Outline 2m
- Introducing Weka 2m
- Demo: Environment and Project Setup 4m
- Demo: Exploring the Weka Workbench 5m
- Demo: Loading and Exploring the Dataset 4m
- Demo: Training and Evaluating a Regression Model 5m
- Demo: Training and Evaluating a Multiple Regression Model 5m
- Demo: Feature Selection and Ranking 7m
- Demo: Processing and Saving Processed Data 5m
- Demo: Evaluating a Model Using Cross Validation 2m
- Demo: Regression Using Support Vector Machines and Multilayer Perceptrons 4m
- Demo: Serializing and Visualizing a Decision Tree Model 6m
- Demo: Normalizing and Visualizing Data 6m
- Demo: Performing K-means Clustering 4m
- Demo: Visualizing Cluster Assignments 6m
- Demo: Exploring and Visualizing Data 3m
- Demo: Performing Hierarchical Clustering 5m
- Demo: Performing EM Clustering 4m
- Demo: Serializing Trained Model Parameters 3m
- Demo: Deploying a Model Using SpringBoot 7m
- Summary and Further Study 2m
About the author
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