开始时间: 04/22/2022 持续时间: 4 weeks
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所在平台: CourseraArchive 课程类别: 信息，技术与设计 大学或机构: Johns Hopkins University(约翰•霍普金斯大学) 授课老师： Jeff Leek |
课程主页: https://www.coursera.org/course/predmachlearn
课程评论：没有评论
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Upon completion of this course you will understand the components of a machine learning algorithm. You will also know how to apply multiple basic machine learning tools. You will also learn to apply these tools to build and evaluate predictors on real data.
Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.
