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
约翰霍普金斯大学課程信息
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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）
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體驗 Coursera 企業版您將學到的內容有
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 Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
可靈活調整截止日期
根據您的日程表重置截止日期。
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。
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）
對員工進行熱門技能培訓能否為您的公司帶來益處？
體驗 Coursera 企業版
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.
授課大綱 - 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
- 5 stars66.42%
- 4 stars22.36%
- 3 stars6.91%
- 2 stars2.52%
- 1 star1.77%
來自实用机器学习的熱門評論
由 CJ 提供2018年5月11日
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..
由 LS 提供2018年2月3日
The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.
由 FF 提供2016年7月8日
Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.
由 UY 提供2016年11月16日
Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.
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