Practical Machine Learning

提供方

Johns Hopkins University

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....

Course 8 of 10 in the

Data Science專項課程

100% 在線課程

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。

建議：4 hours/week

完成時間大約為13 小時

English

字幕：English

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

Course 8 of 10 in the

Data Science專項課程

100% 在線課程

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。

建議：4 hours/week

完成時間大約為13 小時

English

字幕：English

教學大綱 - 您將從這門課程中學到什麼

1

章節

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（共 73 分鐘）, 3 個閱讀材料, 1 個測驗

9 個視頻

Prediction motivation8分鐘

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

2

章節

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（共 96 分鐘）, 1 個測驗

9 個視頻

Caret package6分鐘

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

3

章節

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（共 48 分鐘）, 1 個測驗

5 個視頻

Predicting with trees12分鐘

Bagging9分鐘

Random Forests6分鐘

Boosting7分鐘

Model Based Prediction11分鐘

1 個練習

Quiz 310分鐘

4

章節

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（共 33 分鐘）, 2 個閱讀材料, 3 個測驗

4 個視頻

Regularized regression13分鐘

Combining predictors7分鐘

Forecasting7分鐘

Unsupervised Prediction4分鐘

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

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....

常見問題

When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?

還有其他問題嗎？請訪問學生幫助中心。

Practical Machine Learning

Course 8 of 10 in the

100% 在線課程

可靈活調整截止日期

建議：4 hours/week

English

您將學到的內容有

您將獲得的技能

Course 8 of 10 in the

100% 在線課程

可靈活調整截止日期

建議：4 hours/week

English

教學大綱 - 您將從這門課程中學到什麼

1

Week 1: Prediction, Errors, and Cross Validation

2

Week 2: The Caret Package

3

Week 3: Predicting with trees, Random Forests, & Model Based Predictions

4

Week 4: Regularized Regression and Combining Predictors

34%

83%

14%

熱門審閱

講師

Jeff Leek, PhD

Roger D. Peng, PhD

Brian Caffo, PhD

關於 Johns Hopkins University

關於 Data Science 專項課程

常見問題