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.
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约翰霍普金斯大学课程信息
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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）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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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 Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
可灵活调整截止日期
根据您的日程表重置截止日期。
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。
完成时间大约为9 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
对员工进行热门技能培训能否为您的公司带来益处？
体验 Coursera 企业版
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.
授课大纲 - 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
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）
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）
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
审阅
- 5 stars66.42%
- 4 stars22.36%
- 3 stars6.91%
- 2 stars2.52%
- 1 star1.77%
来自实用机器学习的热门评论
由 JP 提供Jun 24, 2017
Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.
由 NK 提供Feb 18, 2016
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.
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.
由 CJ 提供May 11, 2018
This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..
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