courseraАнализ данных

Interpretable Machine Learning Applications: Part 4

Курс от Coursera

Средний≈ 1.9 чАнглийский

О курсе

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered.

Навыки, которые вы освоите

Data Preprocessing Decision Tree Learning Random Forest Algorithm Classification Algorithms Statistical Visualization Analysis Software Visualization Data Import/Export Data Analysis Model Training Machine Learning Methods Applied Machine Learning Data Processing Machine Learning Model Evaluation Google Cloud Platform Predictive Modeling

Программа курса

1 модулей · 4 учебных материалов

01 Introducing the What-If Tool as Interpretable Machine Learning Application4 материалов

Practical Application via Rhyme

Project-based Course OverviewЧтение Interpretable Machine Learning Applications: Part 4Лабораторная Practice Quiz: Test your project understanding Задание