实用机器学习

Practical Machine Learning

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.

约翰霍普金斯大学

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实用机器学习
  • 分类: 计算机
  • 平台: Coursera
  • 语言: 英语

课程简介

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.

课程大纲

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.

背景知识

先学习“数据科学家的常用工具”和“R语言编程”等“Data Science” 专项课程

授课形式

There will be weekly video lectures, quizzes, and peer assessments.

As part of this class you will be required to set up a GitHub account. GitHub is a tool for collaborative code sharing and editing. During this course and other courses in the Specialization you will be submitting links to files you publicly place in your GitHub account as part of peer evaluation. If you are concerned about preserving your anonymity you will need to set up an anonymous GitHub account and be careful not to include any information you do not want made available to peer evaluators.

常见问题

学完这门课,我能得到结课证书吗?
凡顺利完成本课程的学生均可获得由授课老师签发的结课证书。

选修这门课需要准备什么?
学生必须拥有一个激活的Github账户,并安装最新版的R语言和RStudio软件。

这门课与数据科学系列课程之间应该怎样合理安排?
这是该系列课程的第8部,我们强烈建议大家先学习《数据科学家的常用工具》和《R语言编程》、《回归模型》、《探索性数据分析》这几门课程。

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