Is Training, Evaluating, and Monitoring Machine Learning Models suitable for beginners?

This course is designed for learners with some experience in programming and machine learning. It focuses on techniques used to evaluate and maintain ML models in real-world systems.

What evaluation techniques will I learn in Training, Evaluating, and Monitoring Machine Learning Models?

You'll learn how to use performance metrics, confusion matrices, residual analysis, and statistical evaluation techniques to assess model performance and diagnose prediction errors.

Why is monitoring important for machine learning systems?

Models can degrade over time as data changes. Monitoring helps detect issues such as model drift or performance drops so teams can retrain or update models before problems affect users.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Training, Evaluating, and Monitoring Machine Learning Models

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Training, Evaluating, and Monitoring Machine Learning Models

This course is part of Machine Learning Made Easy for Software Engineers Specialization

Instructor: Professionals from the Industry

Included with

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10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Train machine learning models and analyze training dynamics using logs and loss curves
Evaluate model performance using metrics, confusion matrices, and statistical analysis
Design monitoring strategies to detect model drift and maintain model reliability

Skills you'll gain

Tools you'll learn

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 Machine Learning Made Easy for Software Engineers 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 are 10 modules in this course

Building machine learning models is only the first step. To create reliable ML systems, engineers must evaluate model performance, diagnose prediction errors, and monitor deployed models over time. In this course, you'll learn how to train, evaluate, and monitor machine learning models using practical engineering techniques.

You’ll begin by exploring model training strategies that improve convergence and performance. You’ll analyze training logs, loss curves, and class imbalance effects to understand how models learn and where they struggle. Next, you’ll learn how to evaluate machine learning models using appropriate performance metrics. You’ll analyze confusion matrices and residual patterns to identify systematic prediction errors and assess the statistical significance of model improvements. Finally, you’ll focus on monitoring machine learning models in production environments. You’ll apply validation techniques, analyze A/B testing results, and monitor model behavior over time to detect performance drift and trigger retraining workflows. Through a hands-on project, you'll design a model evaluation and monitoring framework that helps ensure machine learning systems remain accurate and reliable after deployment.

Module details

You will apply batch and mini-batch training procedures to optimize model convergence.

What's included

3 videos1 reading1 assignment

You will analyze training logs and loss curves to diagnose common model training issues.

What's included

2 videos1 reading1 ungraded lab

You will evaluate the impact of class-imbalance techniques on model performance.

What's included

1 video1 reading2 assignments

You will apply appropriate performance metrics to evaluate machine learning models.

What's included

2 videos1 reading1 assignment

You will analyze confusion matrices and residual plots to identify systematic model prediction errors.

What's included

2 videos1 reading1 assignment

You will evaluate the statistical significance of differences in metrics.

What's included

2 videos1 reading1 assignment1 ungraded lab

2 videosTotal 10 minutes

Why Statistical Significance Matters in Model Comparison4 minutes
Bootstrapping Metrics Step by Step6 minutes

1 readingTotal 10 minutes

Evaluating Statistical Significance in Automated Model Monitoring 10 minutes

1 assignmentTotal 20 minutes

Graded Quiz: Interpreting Metrics and Model Improvements20 minutes

1 ungraded labTotal 60 minutes

End-to-End Model Evaluation Practice60 minutes

You will apply validation techniques to assess model performance on unseen data.

What's included

2 videos1 reading1 assignment

You will analyze A/B test or shadow deployment results to compare new model performance against a baseline.

What's included

2 videos1 reading1 assignment

You will evaluate model-drift indicators to trigger retraining workflows.

What's included

2 videos1 reading1 assignment1 ungraded lab

In this project, you will design and implement a machine learning model evaluation and monitoring framework for a production system. A technology company has deployed a recommendation model that predicts user engagement with content, but its performance has become inconsistent due to potential data drift and evolving user behavior. Your task is to build an evaluation pipeline that compares model versions, analyzes prediction errors, and monitors performance stability over time. You will train baseline and improved models, analyze training logs and loss curves to verify convergence, evaluate class-imbalance handling techniques to ensure fair evaluation across classes, evaluate them using appropriate metrics, analyze errors with confusion matrices and residual plots, perform statistical comparisons, simulate monitoring scenarios such as A/B testing or shadow deployments, calculate drift indicators like Population Stability Index (PSI), and define conditions for model retraining. The final deliverable is a modular Python evaluation framework along with a written engineering explanation demonstrating how evaluation insights support reliable model deployment decisions.