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.
约翰霍普金斯大学课程信息
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学生职业成果
37%
完成这些课程后已开始新的职业生涯
37%
通过此课程获得实实在在的工作福利
12%
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完成时间大约为9 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, 巴西葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 中文
您将学到的内容有
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
您将获得的技能
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
学生职业成果
37%
完成这些课程后已开始新的职业生涯
37%
通过此课程获得实实在在的工作福利
12%
加薪或升职
可分享的证书
完成后获得证书
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
根据您的日程表重置截止日期。
完成时间大约为9 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, 巴西葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 中文
提供方
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.
教学大纲 - 您将从这门课程中学到什么
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.
完成时间为 2 小时
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 130分钟
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.
完成时间为 2 小时
9 个视频 （总计 96 分钟）
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 230分钟
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.
完成时间为 1 小时
5 个视频 （总计 48 分钟）
1 个练习
Quiz 330分钟
完成时间为 3 小时
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
完成时间为 3 小时
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 430分钟
Course Project Prediction Quiz30分钟
审阅
来自实用机器学习的热门评论
由 MR 提供Aug 13, 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
由 AD 提供Feb 28, 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.
由 DH 提供Jun 17, 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.
由 AS 提供Aug 30, 2017
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.
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