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Certificate
Course Outline
Overview
Initial Setup for KNIME
- Definition of KNIME
- KNIME Analytics Platform
- KNIME Server Capabilities
Machine Learning Fundamentals
- Principles of computational learning theory
- Algorithms for computational experience and analysis
Establishing the Development Environment
- Installation and configuration procedures for KNIME
KNIME Node Operations
- Implementation of nodes
- Data access and ingestion methods
- Techniques for merging, splitting, and filtering data sets
- Procedures for grouping and pivoting data
- Data cleaning protocols
Model Development
- Workflow creation procedures
- Data import mechanisms
- Data preparation standards
- Data visualization techniques
- Construction of decision tree models
- Application of regression models
- Data prediction methodologies
- Comparative and matching analysis for data sets
Analytical Techniques
- Application of random forest algorithms
- Utilization of polynomial regression
- Class assignment processes
- Model evaluation criteria
Summary and Concluding Remarks
Requirements
- Competency in Python programming
- Proficiency with the R language
Target Audience
- Data Scientists
14 Hours
Testimonials (3)
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Equipped with examples