课程信息
4.5
2,223 个评分
435 个审阅
专项课程

第 8 门课程(共 10 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为14 小时

建议:4 hours/week...
可选语言

英语(English)

字幕:英语(English)

您将学到的内容有

  • Check

    Describe machine learning methods such as regression or classification trees

  • Check

    Explain the complete process of building prediction functions

  • Check

    Understand concepts such as training and tests sets, overfitting, and error rates

  • Check

    Use the basic components of building and applying prediction functions

您将获得的技能

Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
专项课程

第 8 门课程(共 10 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
完成时间(小时)

完成时间大约为14 小时

建议:4 hours/week...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation....
Reading
9 个视频 (总计 73 分钟), 3 个阅读材料, 1 个测验
Video9 个视频
What is prediction?8分钟
Relative importance of steps9分钟
In and out of sample errors6分钟
Prediction study design9分钟
Types of errors10分钟
Receiver Operating Characteristic5分钟
Cross validation8分钟
What data should you use?6分钟
Reading3 个阅读材料
Welcome to Practical Machine Learning10分钟
Syllabus10分钟
Pre-Course Survey10分钟
Quiz1 个练习
Quiz 110分钟
2
完成时间(小时)
完成时间为 2 小时

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing....
Reading
9 个视频 (总计 96 分钟), 1 个测验
Video9 个视频
Data slicing5分钟
Training options7分钟
Plotting predictors10分钟
Basic preprocessing10分钟
Covariate creation17分钟
Preprocessing with principal components analysis14分钟
Predicting with Regression12分钟
Predicting with Regression Multiple Covariates11分钟
Quiz1 个练习
Quiz 210分钟
3
完成时间(小时)
完成时间为 1 小时

Week 3: Predicting with trees, Random Forests, & Model Based Predictions

This week we introduce a number of machine learning algorithms you can use to complete your course project....
Reading
5 个视频 (总计 48 分钟), 1 个测验
Video5 个视频
Bagging9分钟
Random Forests6分钟
Boosting7分钟
Model Based Prediction11分钟
Quiz1 个练习
Quiz 310分钟
4
完成时间(小时)
完成时间为 4 小时

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors. ...
Reading
4 个视频 (总计 33 分钟), 2 个阅读材料, 3 个测验
Video4 个视频
Combining predictors7分钟
Forecasting7分钟
Unsupervised Prediction4分钟
Reading2 个阅读材料
Course Project Instructions (READ FIRST)10分钟
Post-Course Survey10分钟
Quiz2 个练习
Quiz 410分钟
Course Project Prediction Quiz40分钟
4.5
435 个审阅Chevron Right
职业方向

34%

完成这些课程后已开始新的职业生涯
工作福利

33%

通过此课程获得实实在在的工作福利
职业晋升

14%

加薪或升职

热门审阅

创建者 ADMar 1st 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

创建者 DHJun 18th 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

讲师

Avatar

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

关于 Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

关于 Data Science 专项课程

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material....
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