This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.
Automate ML Pipelines for Peak Performance
Automate ML Pipelines for Peak Performance
This course is part of ML Production Systems Specialization
Instructor: ansrsource instructors
Included with
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Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
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Recently updated!
March 2026
Taught in English
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This course is part of the ML Production Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
This course teaches you how to build a fully automated machine learning pipeline using scikit-learn. You will learn to scale numeric features, encode categorical variables, train a logistic model, and optimize it using GridSearchCV. The course then guides you in packaging the workflow as a reusable module that fits real-world ML engineering and MLOps practices. Through concise videos, structured readings, two 15-minute Coach interactions, a combined 25-minute hands-on activity, and a 45-minute ungraded lab, you will practice constructing and refining an end-to-end pipeline. By the end, you will have a polished, automated workflow you can reuse, adapt, and integrate into your ML projects or production systems.
What's included
4 videos2 readings2 assignments1 ungraded lab
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