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
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Automate ML Pipelines for Peak Performance
This course is part of ML Production Systems Specialization
Instructor: ansrsource instructors
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Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
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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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