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Certificate
Duration 14 hours
Course Outline
Introduction
Initiation with Knime
- Overview of KNIME
- KNIME Analytics
- KNIME Server
Machine Learning Fundamentals
- Computational learning theory
- Algorithms for computational experience
Setting Up the Development Environment
- Installation and configuration of KNIME
Managing KNIME Nodes
- Incorporating nodes into workflows
- Retrieving and interpreting data
- Combining, dividing, and refining datasets
- Aggregating and restructuring data
- Data cleaning procedures
Model Development
- Constructing workflows
- Data importation
- Data preparation
- Data visualization
- Building decision tree models
- Application of regression models
- Data prediction techniques
- Data comparison and matching
Advanced Learning Techniques
- Application of random forest methods
- Utilization of polynomial regression
- Class assignment
- Model evaluation
Recap and Final Remarks
Requirements
- Proficiency in Python
- Familiarity with R
Target Audience
- Data Scientists
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.
