About this Course

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Learner Career Outcomes

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got a tangible career benefit from this course

12%

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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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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

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. 9 hours to complete
English

Offered by

Placeholder

Johns Hopkins University

Syllabus - What you will learn from this course

Content RatingThumbs Up89%(6,237 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 130m
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 230m
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 330m
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
4 videos
Combining predictors7m
Forecasting7m
Unsupervised Prediction4m
2 readings
Course Project Instructions (READ FIRST)10m
Post-Course Survey10m
2 practice exercises
Quiz 430m
Course Project Prediction Quiz30m

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