courseraАнализ данных

Interpretable Machine Learning Applications: Part 1

Курс от Coursera

Начальный≈ 1.7 чАнглийский

О курсе

In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model. The project will also benefit your career as a decision maker in an executive position, or consultant, interested in deploying trusted and accountable ML applications.

Навыки, которые вы освоите

Random Forest Algorithm Applied Machine Learning Decision Tree Learning Data Preprocessing Machine Learning Classification Algorithms Feature Engineering Model Training Data Import/Export Responsible AI Classification And Regression Tree (CART)Predictive Modeling Model Evaluation

Программа курса

1 модулей · 4 учебных материалов

01Interpretable machine learning applications: Part 14 материалов

Interpretable Machine Learning Applications: Part 1

Interpretable machine learning applications: Part 1Чтение Interpretable machine learning applications: Part 1Лабораторная Test your knowledge!Задание

Interpretable Machine Learning Applications: Part 1

О курсе

Навыки, которые вы освоите

Программа курса

Interpretable Machine Learning Applications: Part 1

Учитесь у экспертов

Epaminondas Kapetanios