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.
约翰霍普金斯大学課程信息
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學生職業成果
37%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
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完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
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完成時間大約為9 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將學到的內容有
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
您將獲得的技能
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
學生職業成果
37%
完成這些課程後已開始新的職業生涯
37%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為9 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 2 小時
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
完成時間為 2 小時
9 個視頻 （總計 73 分鐘）, 4 個閱讀材料, 1 個測驗
完成時間為 2 小時
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
完成時間為 2 小時
9 個視頻 （總計 96 分鐘）
完成時間為 1 小時
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.
完成時間為 1 小時
5 個視頻 （總計 48 分鐘）
完成時間為 3 小時
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
完成時間為 3 小時
4 個視頻 （總計 33 分鐘）, 2 個閱讀材料, 3 個測驗
審閱
來自实用机器学习的熱門評論
由 MR 提供2020年8月13日
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2017年2月28日
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2018年6月17日
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2017年8月30日
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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完成课程后，我会获得大学学分吗？
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