Training Data vs Test Data Explained | Machine Learning Fundamentals

Pubblicato il: 13 febbraio 2026
sul canale di: CloudWolf AWS

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In this tutorial, we explain one of the most important concepts in Machine Learning: training data vs test data.

You’ll learn why we split datasets (commonly 80/20), how models are trained on historical data, and how test data helps evaluate whether a model can generalize to unseen examples — a key concept for the AWS exam.

🔹 Key Topics Covered:

What is training data?

What is test data?

Why we split data (commonly 80% / 20%)

Preventing overfitting

Evaluating model performance on unseen data

Comparing predicted vs actual values

Why test data must remain unseen during training

Exam tip: training vs inference vs evaluation

📘 Perfect for:
AWS exam takers, ML beginners, data analysts, and anyone learning the fundamentals of model evaluation.

All of our courses available at: https://www.cloudwolf.com/

⏱️ Timestamps

00:00 – Intro: Why training vs test data matters
00:25 – Example: predicting secondhand car prices
00:55 – Collecting historical data
01:20 – Splitting data (80% training, 20% test)
01:50 – Training the model
02:15 – Why we need test data
02:45 – Evaluating predictions vs actual values
03:15 – Generalization & overfitting
03:30 – Summary

🧠 Hashtags

#MachineLearning #TrainingData #TestData #MLFundamentals
#AWSExamPrep #CloudWolf #AIConcepts #ModelEvaluation

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