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Certificate
Course Outline
Introduction
Getting Started with Knime
- What is KNIME?
- KNIME Analytics
- KNIME Server
Machine Learning
- Computational learning theory
- Computer algorithms for computational experience
Preparing the Development Environment
- Installing and configuring KNIME
KNIME Nodes
- Adding nodes
- Accessing and reading data
- Merging, splitting, and filtering data
- Grouping and pivoting data
- Cleaning data
Modeling
- Creating workflows
- Importing data
- Preparing data
- Visualizing data
- Creating a decision tree model
- Working with regression models
- Predicting data
- Comparing and matching data
Learning Techniques
- Working with random forest techniques
- Using polynomial regression
- Assigning classes
- Evaluating models
Summary and Conclusion
Requirements
- Experience with Python
- R experience
Audience
- Data Scientists
14 Hours
Testimonials (4)
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.
Ian - Archeoworks Inc.
Course - ArcGIS Fundamentals
All the topics which he covered including examples. And also explained how they are helpful in our daily job.
madduri madduri - Boskalis Singapore Pte Ltd
Course - QGIS for Geographic Information System
I really enjoyed the training. I found all modules to be applicable to problems that I am trying to solve at work. The integration of the training with jupyter notebooks was really impressive.
Mark Firmin - Environment and Climate Change Canada
Course - Python for Geographic Information System (GIS)
The thing I liked the most about the training was the organization and the location
