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
Practical Machine Learning
This course is part of multiple programs.
Instructors: Jeff Leek, PhD
153,882 already enrolled
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Gain insight into a topic and learn the fundamentals.
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
Recommended if you're interested in Machine Learning
Amazon Web Services
University of Colorado Boulder
University of Minnesota
University of California San Diego
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4.5
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Showing 3 of 3246
MC
4
Reviewed on Dec 10, 2017
Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.
ST
5
Reviewed on Mar 6, 2016
I want to learn ML in R so I go straight to this course without taking any other course in this specialization, and it doesn't disappoint me. Thanks for a great course!
EG
4
Reviewed on Jul 27, 2016
I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.
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