About this Course

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Learner Career Outcomes

38%

started a new career after completing these courses

38%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

English

Subtitles: English, Korean

What you will learn

  • 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

Skills you will gain

Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming

Learner Career Outcomes

38%

started a new career after completing these courses

38%

got a tangible career benefit from this course

12%

got a pay increase or promotion

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 8 hours to complete

English

Subtitles: English, Korean

Offered by

Johns Hopkins University logo

Johns Hopkins University

Syllabus - What you will learn from this course

Content RatingThumbs Up89%(5,784 ratings)Info
Week
1

Week 1

2 hours to complete

Week 1: Prediction, Errors, and Cross Validation

2 hours to complete
9 videos (Total 73 min), 4 readings, 1 quiz
9 videos
What is prediction?8m
Relative importance of steps9m
In and out of sample errors6m
Prediction study design9m
Types of errors10m
Receiver Operating Characteristic5m
Cross validation8m
What data should you use?6m
4 readings
Welcome to Practical Machine Learning10m
A Note of Explanation2m
Syllabus10m
Pre-Course Survey10m
1 practice exercise
Quiz 110m
Week
2

Week 2

2 hours to complete

Week 2: The Caret Package

2 hours to complete
9 videos (Total 96 min)
9 videos
Data slicing5m
Training options7m
Plotting predictors10m
Basic preprocessing10m
Covariate creation17m
Preprocessing with principal components analysis14m
Predicting with Regression12m
Predicting with Regression Multiple Covariates11m
1 practice exercise
Quiz 210m
Week
3

Week 3

1 hour to complete

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

1 hour to complete
5 videos (Total 48 min)
5 videos
Bagging9m
Random Forests6m
Boosting7m
Model Based Prediction11m
1 practice exercise
Quiz 310m
Week
4

Week 4

4 hours to complete

Week 4: Regularized Regression and Combining Predictors

4 hours to complete
4 videos (Total 33 min), 2 readings, 3 quizzes
4 videos
Combining predictors7m
Forecasting7m
Unsupervised Prediction4m
2 readings
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
2 practice exercises
Quiz 410m
Course Project Prediction Quiz40m

Frequently Asked Questions

  • 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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  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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