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.
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.
A problem solver at heart, 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.
Course Overview Hi. My name is Janani Ravi, and welcome to this course on implementing the machine learning workflow with Weka. A little about myself, I have a masters degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real‑time collaborative editing in Google Docs, and I hold four patents for its online technologies. I currently work on my own startup, LoonyCorn, a studio for high quality video content. Weka is a tried and tested open source machine learning software for building all components of a machine learning workflow. Weka offers a graphical user interface, terminal applications, as well as a Java API to train models. In this course, first, you'll get started with an Apache Maven project and set up your Java development environment. You will use Weka to build regression models and evaluate them using standard metrics such as the R squared score, you'll perform feature selection and data preprocessing using Weka Java libraries, and implement regression using algorithms such as decision trees and the multilayer perceptron. Next, you will explore building and evaluating classification models in Weka. You will use the naive base classifier and evaluate your classifications models using metrics such as accuracy, precision, and recall. You'll also perform classification on text data. Finally, you'll use Weka to perform clustering using the k‑means clustering algorithm, hierarchical clustering, as well as expectation maximization clustering. You will visualize the resulting clusters using the Weka workbench. You'll also deploy a classifications model in a limited production environment using Spring Boot. When you're finished with this course, you'll have the knowledge and skills to build supervised and unsupervised machine learning models using the Weka Java library.