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Certificate
Course Outline
Introduction
Introduction to KNIME
- Overview of KNIME
- KNIME Analytics
- KNIME Server
Machine Learning Fundamentals
- Computational learning theory
- Algorithms for computational experiences
Setting Up the Development Environment
- Installation and configuration of KNIME
Working with KNIME Nodes
- Adding nodes to workflows
- Data access and retrieval
- Data merging, splitting, and filtering
- Grouping and pivoting data sets
- Data cleaning techniques
Modeling Process
- Constructing workflows
- Data import procedures
- Data preparation methods
- Data visualization strategies
- Building decision tree models
- Utilizing regression models
- Data prediction techniques
- Data comparison and matching
Advanced Learning Techniques
- Application of random forest techniques
- Implementing polynomial regression
- Class assignment processes
- Model evaluation metrics
Wrap-up and Summary
Requirements
- Proficiency in Python
- Familiarity with R
Target Audience
- Data Scientists
14 Hours
Testimonials (3)
Learning that the QGIS and a tool that can used by other different professionals such land survey
Bame Duncan Koko - Bentel Technologies (Pty) Ltd
Course - QGIS for Geographic Information System
How to use open satellites data for real applications
Tshering Dorji - Druk Holding and Investments
Course - Advanced Geographic Information Systems (GIS)
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
