e-learning
Basics of machine learning
Abstract
Machine learning uses techniques from statistics, mathematics and computer science to make computer programs learn from data. It is one of the most popular fields of computer science and finds applications in multiple streams of data analysis such as classification, regression, clustering, dimensionality reduction, density estimation and many more. Some real-life applications are spam filtering, medical diagnosis, autonomous driving, recommendation systems, facial recognition, stock prices prediction and many more. The following image shows a basic flow of any machine learning task. Data is provided by a user to a machine learning algorithm for analysis.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- What is machine learning?
- Why is it useful?
- What are its different approaches?
Learning Objectives
- Provide the basics of machine learning and its variants.
- Learn how to do classification using the training and test data.
- Learn how to use Galaxy's machine learning tools.
Licence: Creative Commons Attribution 4.0 International
Keywords: Statistics and machine learning
Target audience: Students
Resource type: e-learning
Version: 13
Status: Active
Prerequisites:
Introduction to Galaxy Analyses
Learning objectives:
- Provide the basics of machine learning and its variants.
- Learn how to do classification using the training and test data.
- Learn how to use Galaxy's machine learning tools.
Date modified: 2024-03-05
Date published: 2018-11-05
Contributors: Anup Kumar
Scientific topics: Statistics and probability
Activity log
