courseraАнализ данных

Interpretable machine learning applications: Part 5

Курс от Coursera

Начальный≈ 1.6 чАнглийский

О курсе

You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker.

Навыки, которые вы освоите

Model Evaluation Data Visualization Descriptive Statistics Data Ethics Economics, Policy, and Social Studies Histogram Data Preprocessing Predictive Modeling Responsible AI Predictive Analytics Statistical Methods Machine Learning Software Engineering Exploratory Data Analysis Policy Analysis Data Science Development Environment

Программа курса

1 модулей · 4 учебных материалов

01Project Overview4 материалов

Your Learning Journey

Project OverviewЧтение Interpretable machine learning applications: Part 5Лабораторная Assess Your KnowledgeЗадание Course End Survey - We appreciate your feedback!