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Model Training and Evaluation with Liminal

Overview

Skills Needed

Learn to train and evaluate models with Liminal. Explore algorithm selection, hyperparameter tuning, and model validation techniques with Liminal.

  • Intermediate knowledge of Liminal fundamentals
  • Familiarity with model training concepts

Outline

  • Introduction to Model Training and Evaluation
  • Algorithm Selection for ML Models
  • Hyperparameter Tuning Strategies
  • Cross-validation Techniques
  • Model Interpretability and Explainability
  • Ensemble Learning Methods
  • Transfer Learning Approaches
  • Reinforcement Learning Fundamentals
  • Model Optimization Techniques
  • Best Practices for Model Training and Evaluation with Liminal

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contact@datauology.com

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