Machine Learning Using Weka Software | Simpliv
Course by Simpliv LLC
(Course Id: 245071 | 104 Views) Posted 00 UNKCategory:
Education, Information Technology
How the course is given:
Online
When the courses is given:
Flexible
Qualification Level:
Short Course/Training Certification
Course Duration:
01:17:19
Price:
$ 9.99
Location:
Fremont, CA
Registration is Available at Anytime
Course Description
Description
This is the bite size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, Neural Network in this course.
Basic knowledge
- Computer Knowledge
- Basic coding knowledge
What will you learn
Content
- Getting Started
- Getting Started 2
- Data Mining Process
- Simple Linear Regression
- Regression in Weka
- KMeans Clustering
- KMeans Clustering in Weka
- Agglomeration Clustering
- Agglomeration Clustering in Weka
- Decision Tree: ID3 Algorithm
- Decision Tree in Weka
- KNN Classiifcation
- KNN in Weka
- Naive Bayes
- Naive Bayes in Weka
- What Algorithm to use?
- Model Evaluation
- Weka Advanced Attribute Selection
- Weka Advanced Data Visualizations
- Weka Model Selection and Deployment
About the Provider
Simpliv is a global online learning marketplace that transforms lives by offering online training on a wide variety of topics. Created with the aim of making education accessible to all, Simpliv removes barriers to education among all communities, imparts life skills to learners, and bridges gaps in learning through cost-effective courses. Simpliv believes that learning has no boundaries. It brings learning to any person who wants to learn, whether it is management, technology, life sciences,... Read More
Course Provider Contact
Simpliv LLC
39658 Mission Boulevard, Fremont,
CA 94539, USA.
Phone: +510-849-6155
Email: sudheer@simpliv.com
39658 Mission Boulevard, Fremont,
CA 94539, USA.
Phone: +510-849-6155
Email: sudheer@simpliv.com