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Model Training & Evaluation

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Model Training & Evaluation

This course is part of Train, Tune, & Ship: End-to-End Machine Learning Engineering Specialization

Instructor: ansrsource instructors

Included with

Learn more

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Scikit Learn (Machine Learning Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

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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.

Module details

What's included

7 videos3 readings3 assignments1 ungraded lab

7 videosTotal 25 minutes

Introduction and Welcome4 minutes
Why Mini-Batches Improve Training Stability5 minutes
How Schedulers Influence Convergence4 minutes
Reading Loss Curves Like an Analyst3 minutes
Spotting Instability Using Training Logs2 minutes
Choosing Class-Imbalance Methods with Confidence3 minutes
Congratulations and Continuous Learning Journey4 minutes

3 readingsTotal 19 minutes

Batch vs Mini-Batch: What Changes in Practice6 minutes
Common Training Issues and How Logs Reveal Them6 minutes
How Balanced Data Shapes Your Model’s F1 Score7 minutes

3 assignmentsTotal 52 minutes

Hands-On Activity: Train a PyTorch Model with Mini-Batches and Scheduler15 minutes
Hands-On Activity: Compare F1 Scores Using Class-Weights and SMOTE12 minutes
Graded Quiz: Assessing Training, Diagnostics, and Imbalance Methods25 minutes