courseraАнализ данных

Optimize AI: Build & Evaluate Predictive Models

Курс от Coursera

Средний≈ 3 чАнглийский

О курсе

This short course helps you build and evaluate predictive models using supervised and unsupervised techniques. You will practice training algorithms with scikit-learn, explore how cross-validation affects model reliability, and analyze performance metrics like accuracy and F1 to make data-driven improvements. Instead of relying on guesswork, you’ll learn how to iterate systematically so your models meet defined performance targets. Through hands-on labs and guided coaching, you will build logistic-regression and clustering models, apply 5-fold cross-validation, and refine features until your model performs at the level you need. By the end, you will be able to apply these workflows to real predictive modeling tasks in retail and credit-risk contexts.

Навыки, которые вы освоите

Predictive Modeling Performance Metric Model Optimization Performance Analysis Statistical Machine Learning Model Training Machine Learning Methods Performance Improvement

Программа курса

1 модулей · 18 учебных материалов

01Optimize AI: Build & Evaluate Predictive Models18 материалов

Train and Validate Predictive Models with Supervised and Unsupervised Algorithms

Welcome and What You’ll LearnВидео Your Modeling GoalsDIALOGUE Supervised vs. Unsupervised Modeling: When to Use EachВидео How Cross-Validation Improves Model ReliabilityЧтение

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