About this Course

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Approx. 9 hours to complete
English

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
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 9 hours to complete
English

Offered by

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Johns Hopkins University

Syllabus - What you will learn from this course

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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
Week
2

Week 2

2 hours to complete

Week 2: The Caret Package

2 hours to complete
9 videos (Total 96 min)
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)
Week
4

Week 4

3 hours to complete

Week 4: Regularized Regression and Combining Predictors

3 hours to complete
4 videos (Total 33 min), 2 readings, 3 quizzes

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