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.
課程信息
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您將學到的內容有
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Use the basic components of building and applying prediction functions
您將獲得的技能
Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
教學大綱 - 您將從這門課程中學到什麼
週
1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.
9 個視頻 （總計 73 分鐘）, 3 個閱讀材料, 1 個測驗
9 個視頻
What is prediction?8分鐘
Relative importance of steps9分鐘
In and out of sample errors6分鐘
Prediction study design9分鐘
Types of errors10分鐘
Receiver Operating Characteristic5分鐘
Cross validation8分鐘
What data should you use?6分鐘
3 個閱讀材料
Welcome to Practical Machine Learning10分鐘
Syllabus10分鐘
Pre-Course Survey10分鐘
1 個練習
Quiz 110分鐘
週
2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.
9 個視頻 （總計 96 分鐘）, 1 個測驗
9 個視頻
Data slicing5分鐘
Training options7分鐘
Plotting predictors10分鐘
Basic preprocessing10分鐘
Covariate creation17分鐘
Preprocessing with principal components analysis14分鐘
Predicting with Regression12分鐘
Predicting with Regression Multiple Covariates11分鐘
1 個練習
Quiz 210分鐘
週
3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.
5 個視頻 （總計 48 分鐘）, 1 個測驗
1 個練習
Quiz 310分鐘
週
4完成時間為 4 小時
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
4 個視頻 （總計 33 分鐘）, 2 個閱讀材料, 3 個測驗
2 個閱讀材料
Course Project Instructions (READ FIRST)10分鐘
Post-Course Survey10分鐘
2 個練習
Quiz 410分鐘
Course Project Prediction Quiz40分鐘
4.5
492 個審閱40%
完成這些課程後已開始新的職業生涯
39%
通過此課程獲得實實在在的工作福利
13%
加薪或升職
來自实用机器学习的熱門評論
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.
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.
講師
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
關於 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.
關於 数据科学 專項課程
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
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