Section: Model Basics and Training Workflow | ML - Derk Test | CollowDemo

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Section outline

Introduction

This section explains the core mechanics behind how ML models learn from data and why they succeed or fail in real ML work. You’ll connect foundational ideas like parameters, loss, and optimization to practical workflow steps used in ML teams. By the end, you should be able to reason about training results and common pitfalls like overfitting.

Learning Objectives

Explain parameters, learning, generalization, and overfitting in beginner-friendly terms.

Describe train/validation/test splits and justify why each split is needed.

Identify loss functions, optimization, and common model families at a high level.
- Select activity Learning, Generalization & Overfitting
  
  Learning, Generalization & Overfitting Page
- Select activity Quiz: Learning, Generalization & Overfitting
  
  Quiz: Learning, Generalization & Overfitting
- Select activity Train/Val/Test Splits and Evaluation Logic
  
  Train/Val/Test Splits and Evaluation Logic Page
- Select activity Quiz: Train/Val/Test Splits and Evaluation Logic
  
  Quiz: Train/Val/Test Splits and Evaluation Logic
- Select activity Loss, Optimization & Model Families
  
  Loss, Optimization & Model Families Page
- Select activity Quiz: Loss, Optimization & Model Families
  
  Quiz: Loss, Optimization & Model Families