What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of multiple programs.
Instructors: Jeff Leek, PhD
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(3,248 reviews)
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
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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.
This week will cover prediction, relative importance of steps, errors, and cross validation.
9 videos4 readings1 assignment
This week will introduce the caret package, tools for creating features and preprocessing.
9 videos1 assignment
This week we introduce a number of machine learning algorithms you can use to complete your course project.
5 videos1 assignment
This week, we will cover regularized regression and combining predictors.
4 videos2 readings2 assignments1 peer review
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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.
Duke University
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Fractal Analytics
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3,248 reviews
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22.35%
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1.78%
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Reviewed on Aug 13, 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
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.
Reviewed on Jun 24, 2017
Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.
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