YoVDO

Evaluating Model Effectiveness in Microsoft Azure

Offered By: Pluralsight

Tags

Microsoft Azure Courses Data Science Courses Overfitting Courses Model Evaluation Courses Model Interpretability Courses

Course Description

Overview

This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.

Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.

Taught by

Tim Warner

Related Courses

Practical Machine Learning
Johns Hopkins University via Coursera
Practical Deep Learning For Coders
fast.ai via Independent
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
National Taiwan University via Coursera
Data Analytics Foundations for Accountancy II
University of Illinois at Urbana-Champaign via Coursera
Entraînez un modèle prédictif linéaire
CentraleSupélec via OpenClassrooms