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）
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）
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 stars66.44%
- 4 stars22.36%
- 3 stars6.93%
- 2 stars2.49%
- 1 star1.74%
來自实用机器学习的熱門評論
由 NK 提供2016年2月18日
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 EG 提供2016年7月27日
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
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.
由 PL 提供2018年9月19日
Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.
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