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Certificate
Course Outline
Introduction
Getting Started with KNIME
- Understanding KNIME.
- Overview of KNIME Analytics.
- Introduction to KNIME Server.
Machine Learning Fundamentals
- Computational learning theory.
- Computer algorithms for computational experience.
Setting Up the Development Environment
- Installing and configuring KNIME.
Working with KNIME Nodes
- Adding nodes.
- Accessing and reading data.
- Merging, splitting, and filtering data.
- Grouping and pivoting data.
- Data cleaning techniques.
Modeling
- Creating workflows.
- Importing data.
- Preparing data for analysis.
- Visualizing data.
- Building a decision tree model.
- Working with regression models.
- Predicting outcomes.
- Comparing and matching data.
Learning Techniques
- Utilizing random forest techniques.
- Applying polynomial regression.
- Assigning classes.
- Evaluating model performance.
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.
