courseraАнализ данных

Build Predictive & Supervised Models

Курс от Coursera

Средний≈ 3.7 чАнглийский

О курсе

Transform your data science career by mastering production-ready machine learning workflows. This Short Course was created to help data analysis professionals accomplish reliable demand forecasting and model governance in business environments. By completing this course, you'll be able to build robust random forest models that hit business targets, implement automated model monitoring systems, and create reproducible ML pipelines that stand the test of time. By the end of this course, you will be able to: - Build cross-validated random forest models that achieve business-defined accuracy targets Evaluate and monitor model drift using statistical metrics to ensure long-term reliability Implement standardized cross-validation pipelines for multiple supervised algorithms Assess feature selection techniques to balance model accuracy with interpretability This course is unique because it bridges the gap between academic machine learning and real-world production requirements, emphasizing business metrics and operational reliability. To be successful in this project, you should have a background in Python programming and basic statistics.

Навыки, которые вы освоите

Feature Engineering Model Evaluation Model Training Applied Machine Learning Data Preprocessing Continuous Monitoring Business Metrics Regression Analysis Predictive Modeling Supervised Learning MLOps (Machine Learning Operations)Random Forest Algorithm Performance Metric Machine Learning Algorithms Scikit Learn (Machine Learning Library)Statistical Methods

Программа курса

4 модулей · 24 учебных материалов

01Module 1: Random Forest Model Building - Foundation6 материалов

The Business Case for Production-Ready Random Forest ModelsDIALOGUE Random Forest Fundamentals for Business ApplicationsЧтение Random Forest Implementation Strategies for Demand ForecastingВидео Building Random Forest Models with Scikit-LearnВидео

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