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Certificate
Course Outline
Introduction
Getting Started with KNIME
- Understanding what KNIME is
- Overview of KNIME Analytics
- Overview of KNIME Server
Machine Learning
- Foundations of computational learning theory
- Computer algorithms designed for computational experience
Preparing the Development Environment
- Installing and configuring KNIME
Working with KNIME Nodes
- Adding nodes to the workflow
- Accessing and reading data
- Merging, splitting, and filtering data
- Grouping and pivoting data
- Cleaning data for analysis
Modeling
- Creating workflows
- Importing data
- Preparing data
- Visualizing data
- Creating a decision tree model
- Working with regression models
- Predicting data outcomes
- Comparing and matching data sets
Learning Techniques
- Utilizing random forest techniques
- Applying polynomial regression
- Assigning classes
- Evaluating models
Summary and Conclusion
Requirements
- Prior experience with Python
- Prior experience with R
Target Audience
- Data Scientists
14 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
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.
