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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 (5)
it was informative and useful
Brenton - Lotterywest
Course - Building Web Applications in R with Shiny
The involvement of the trainer and good preparation of the topic.
Bruno Scibilia - Lesaffre International
Course - Advanced R Programming
Machine Translated
Very useful in because it helps me understand what we can do with the data in our context. It will also help me
Nicolas NEMORIN - Adecco Groupe France
Course - KNIME Analytics Platform for BI
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
I really enjoyed the knowledge of the trainer.
