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 [Autogenerated] Hi, My name is John. Any Ravi and welcome to the scores on implementing the machine. Learning workflow with Becca a little about myself. I have a master's degree in electrical engineering from Stanford on have worked at companies such as Microsoft, Google and Flip Cart at Google. A was one of the first engineers working on real time collaborative editing in Google Dogs and I hold four patterns for its online technologies. I currently work on my own startup, Loony Con, a studio for high quality video content. Vaca is a tried and tested open source machine learning software for building all components off a machine learning workflow. Becca offers a graphical user interface terminal applications, as well as a Java API to train models in this course. First, you'll get started within a party maven project and set up your job. A development environment. You will use Vaca to build regression models and evaluate them. Using standard metrics such as the R Squared score, you'll perform feature selection and data pre processing using Becca, job A libraries and implement regression using algorithms such as decision trees on the multi layer perceptron. Next, you will explore building and evaluating classifications, models and Becca. You will use the naive based classifier and evaluate your classifications models using metrics such as accuracy, precision and recall. You'll also perform classifications on text data. Finally, you'll use record to perform clustering using the K means clustering algorithm. Hierarchical clustering as fellas expectation maximization clustering. You will visualize the resulting clusters using the week aboard bench. 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 Vaca Job a library.