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 Duration 14 hours

Course Outline

Introduction

Initiating the KNIME Workflow

  • Definition of KNIME
  • KNIME Analytics Platform
  • KNIME Server Infrastructure

Machine Learning Fundamentals

  • Theoretical foundations of computational learning
  • Algorithmic approaches to computational experience

Development Environment Configuration

  • Installation and system setup of KNIME

KNIME Node Management

  • Integration of nodes into workflows
  • Data retrieval and parsing
  • Consolidation, segmentation, and data filtering
  • Grouping and pivot table creation
  • Data purification and quality assurance

Modeling Processes

  • Workflow architecture and design
  • Data ingestion procedures
  • Data preprocessing for analysis
  • Data visualization techniques
  • Development of decision tree models
  • Application of regression modeling
  • Data forecasting and prediction
  • Data comparison and reconciliation

Advanced Learning Methodologies

  • Application of random forest algorithms
  • Polynomial regression implementation
  • Class assignment strategies
  • Model performance evaluation

Summary and Conclusions

Requirements

  • Proficiency in Python
  • Proficiency in R

Intended Audience

  • Data Scientists

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