About this Course
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Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 14 hours to complete

English

Subtitles: English, Korean

What you will learn

  • Check

    Describe machine learning methods such as regression or classification trees

  • Check

    Explain the complete process of building prediction functions

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    Understand concepts such as training and tests sets, overfitting, and error rates

  • Check

    Use the basic components of building and applying prediction functions

Skills you will gain

Random ForestMachine Learning (ML) AlgorithmsMachine LearningR Programming

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 14 hours to complete

English

Subtitles: English, Korean

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Week 1: Prediction, Errors, and Cross Validation

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
2 hours to complete

Week 2: The Caret Package

9 videos (Total 96 min), 1 quiz
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
1 hour to complete

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

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

Week 4: Regularized Regression and Combining Predictors

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
4.5
498 ReviewsChevron Right

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started a new career after completing these courses

38%

got a tangible career benefit from this course

12%

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Top reviews from Practical Machine Learning

By 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.

By 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.

Instructors

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Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

About 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....

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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