Automate ML Pipelines for Peak Performance

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

• Scale numeric features and encode categorical variables properly
• Train logistic models and optimize using GridSearchCV
• Package end-to-end pipelines as reusable modules
• Build automated workflows aligned with ML engineering and MLOps practices

• Basic familiarity with machine learning concepts and terminology
• Readiness for applied exercises or case-based practice