About this Course
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Course 8 of 10 in the

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 14 hours to complete

English

Subtitles: English

What you will learn

  • Check

    Describe machine learning methods such as regression or classification trees

  • Check

    Explain the complete process of building prediction functions

  • Check

    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

Course 8 of 10 in the

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

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation.

...
9 videos (Total 73 min), 3 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
3 readings
Welcome to Practical Machine Learning10m
Syllabus10m
Pre-Course Survey10m
1 practice exercise
Quiz 110m
Week
2
2 hours to complete

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing.

...
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

This week we introduce a number of machine learning algorithms you can use to complete your course project.

...
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

This week, we will cover 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
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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 ASAug 31st 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

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....

About the Data Science Specialization

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....
Data Science

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.

More questions? Visit the Learner Help Center.