courseraАнализ данных

Automate ML Pipelines for Peak Performance

Курс от Coursera

Средний≈ 2.9 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Model Training Model Optimization MLOps (Machine Learning Operations)Predictive Modeling Feature Engineering Workflow Management

Программа курса

1 модулей · 11 учебных материалов

01Automate ML Pipelines for Peak Performance11 материалов

Build, Optimize, and Publish an Automated ML Pipeline

Why Automation Improves ML PerformanceВидео Interactive Dialogue: Where Does Your ML Workflow Slow Down?DIALOGUE Pipeline Fundamentals: Scaling, Encoding, and Workflow StructureВидео Building a Strong Foundation: Preprocessing, Logistic Regression, and Workflow SetupЧтение

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