One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Practical Machine Learning
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Practical Machine Learning
This course is part of multiple programs.
Instructors: Jeff Leek, PhD
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4.5
(3,246 reviews)
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
89%
Most learners liked this course
What you'll 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
Details to know
Shareable certificate
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Assessments
5 assignments
Taught in English
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There are 4 modules in this course
This week will cover prediction, relative importance of steps, errors, and cross validation.
What's included
9 videos4 readings1 assignment
This week will introduce the caret package, tools for creating features and preprocessing.
What's included
9 videos1 assignment
This week we introduce a number of machine learning algorithms you can use to complete your course project.
What's included
5 videos1 assignment
This week, we will cover regularized regression and combining predictors.
What's included
4 videos2 readings2 assignments1 peer review
Instructors
Instructor ratings
4.5 (132 ratings)Offered by
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