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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

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