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⇱ Model Training & Evaluation | Coursera

Model Training & Evaluation

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

February 2026

Assessments

3 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Train, Tune, & Ship: End-to-End Machine Learning Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There is 1 module in this course

In this short course, you’ll learn how to train and evaluate machine learning models with confidence. You’ll explore how mini-batch training and learning-rate schedulers shape convergence, how to read loss curves and logs to diagnose issues, and how class-imbalance techniques affect F1 scores. Through hands-on PyTorch practice, you’ll train models, investigate instability, and compare weighting and SMOTE. By the end, you’ll understand how to guide models toward stable, reliable performance.

In this short course, you’ll learn how to train and evaluate machine learning models with confidence. You’ll explore how mini-batch training and learning-rate schedulers shape convergence, how to read loss curves and logs to diagnose issues, and how class-imbalance techniques affect F1 scores. Through hands-on PyTorch practice, you’ll train models, investigate instability, and compare weighting and SMOTE. By the end, you’ll understand how to guide models toward stable, reliable performance.

What's included

7 videos3 readings3 assignments1 ungraded lab

7 videos•Total 25 minutes
  • Introduction and Welcome•4 minutes
  • Why Mini-Batches Improve Training Stability•5 minutes
  • How Schedulers Influence Convergence•4 minutes
  • Reading Loss Curves Like an Analyst•3 minutes
  • Spotting Instability Using Training Logs•2 minutes
  • Choosing Class-Imbalance Methods with Confidence•3 minutes
  • Congratulations and Continuous Learning Journey•4 minutes
3 readings•Total 19 minutes
  • Batch vs Mini-Batch: What Changes in Practice•6 minutes
  • Common Training Issues and How Logs Reveal Them•6 minutes
  • How Balanced Data Shapes Your Model’s F1 Score•7 minutes
3 assignments•Total 52 minutes
  • Hands-On Activity: Train a PyTorch Model with Mini-Batches and Scheduler•15 minutes
  • Hands-On Activity: Compare F1 Scores Using Class-Weights and SMOTE•12 minutes
  • Graded Quiz: Assessing Training, Diagnostics, and Imbalance Methods•25 minutes
1 ungraded lab•Total 60 minutes
  • Fix Overfitting by Analyzing Divergence Patterns•60 minutes

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Frequently asked questions

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.