課程信息
4.5
2,086 ratings
407 reviews
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....
Stacks

Course 8 of 10 in the

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100% 在線課程

立即開始,按照自己的計劃學習。
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建議:4 hours/week

完成時間大約為13 小時
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English

字幕:English

您將學到的內容有

  • Check
    Describe machine learning methods such as regression or classification trees
  • Check
    Explain the complete process of building prediction functions
  • Check
    Understand concepts such as training and tests sets, overfitting, and error rates
  • Check
    Use the basic components of building and applying prediction functions

您將獲得的技能

Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming
Stacks

Course 8 of 10 in the

Globe

100% 在線課程

立即開始,按照自己的計劃學習。
Calendar

可靈活調整截止日期

根據您的日程表重置截止日期。
Clock

建議:4 hours/week

完成時間大約為13 小時
Comment Dots

English

字幕:English

教學大綱 - 您將從這門課程中學到什麼

1

章節
Clock
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....
Reading
9 個視頻(共 73 分鐘), 3 個閱讀材料, 1 個測驗
Video9 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
Reading3 個閱讀材料
Welcome to Practical Machine Learning10分鐘
Syllabus10分鐘
Pre-Course Survey10分鐘
Quiz1 個練習
Quiz 110分鐘

2

章節
Clock
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....
Reading
9 個視頻(共 96 分鐘), 1 個測驗
Video9 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
Quiz1 個練習
Quiz 210分鐘

3

章節
Clock
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....
Reading
5 個視頻(共 48 分鐘), 1 個測驗
Video5 個視頻
Bagging9分鐘
Random Forests6分鐘
Boosting7分鐘
Model Based Prediction11分鐘
Quiz1 個練習
Quiz 310分鐘

4

章節
Clock
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. ...
Reading
4 個視頻(共 33 分鐘), 2 個閱讀材料, 3 個測驗
Video4 個視頻
Combining predictors7分鐘
Forecasting7分鐘
Unsupervised Prediction4分鐘
Reading2 個閱讀材料
Course Project Instructions (READ FIRST)10分鐘
Post-Course Survey10分鐘
Quiz2 個練習
Quiz 410分鐘
Course Project Prediction Quiz40分鐘
4.5
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34%

完成這些課程後已開始新的職業生涯
Briefcase

83%

通過此課程獲得實實在在的工作福利
Money

14%

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創建者 ADMar 1st 2017

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.

創建者 DHJun 18th 2018

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, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

關於 Johns Hopkins University

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 Data Science 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....
Data Science

常見問題

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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