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.
提供方
约翰霍普金斯大学
课程信息
64,891 次近期查看
学习Course的学生是
- Risk Managers
- Biostatisticians
- Data Scientists
- Data Analysts
- Biologists
您将学到的内容有
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Use the basic components of building and applying prediction functions
您将获得的技能
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
教学大纲 - 您将从这门课程中学到什么
周
1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.
9 个视频 （总计 73 分钟）, 4 个阅读材料, 1 个测验
9 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
4 个阅读材料
Welcome to Practical Machine Learning10分钟
A Note of Explanation2分钟
Syllabus10分钟
Pre-Course Survey10分钟
1 个练习
Quiz 110分钟
周
2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.
9 个视频 （总计 96 分钟）, 1 个测验
9 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
1 个练习
Quiz 210分钟
周
3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.
5 个视频 （总计 48 分钟）, 1 个测验
1 个练习
Quiz 310分钟
周
4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.
4 个视频 （总计 33 分钟）, 2 个阅读材料, 3 个测验
2 个阅读材料
Course Project Instructions (READ FIRST)10分钟
Post-Course Survey10分钟
2 个练习
Quiz 410分钟
Course Project Prediction Quiz40分钟
4.5
498 个审阅39%
完成这些课程后已开始新的职业生涯
38%
通过此课程获得实实在在的工作福利
12%
加薪或升职
来自实用机器学习的热门评论
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.
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
讲师
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
关于 约翰霍普金斯大学
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.
常见问题
我什么时候能够访问课程视频和作业？
注册以便获得证书后，您将有权访问所有视频、测验和编程作业（如果适用）。只有在您的班次开课之后，才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程，可能无法访问某些作业。
我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
退款政策是如何规定的？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
