Machine Learning for Data Analysis

开始时间: 08/01/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/machine-learning-data-analysis

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课程详情

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

机器学习进行数据分析:您是否有兴趣使用数据预测未来结果?本课程可以帮助您做到这一点!机器学习是开发,测试和应用预测算法以实现此目标的过程。在深入研究这些机器学习概念之前,请确保熟悉本专业的课程3。在课程3的基础上,该课程向学生介绍了完整的监督式机器学习概念,该课程将概述机器学习中的许多其他概念,技术和算法,从基本分类到决策树和聚类。通过完成本课程,您将学习如何应用,测试和解释机器学习算法,作为解决研究问题的替代方法。

课程大纲

In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable.

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课程简介

这门课程关注数据分析里的机器学习,机器学习的过程是一个开发、测试和应用预测算法来实现目标的过程,这门课程以 Regression Modeling in Practice(回归模型实战) 为基础,介绍机器学习中的有监督学习概念,同时从基础的分类算法到决策以及聚类都会覆盖。通过完成这门课程,你将会学习如何应用、测试和解读机器学习算法用来解决实际问题。

课程标签

机器学习 数据分析 面向数据分析的机器学习 回归模型 分类算法 回归 分类 决策树 分类模型 机器学习算法 有监督学习

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