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 +2 more
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4.5
(3,257 reviews)
8 hours to complete
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
Skills you'll gain
- Category: Classification And Regression Tree (CART)
- Category: Regression Analysis
- Category: R Programming
- Category: Applied Machine Learning
- Category: Feature Engineering
- Category: Machine Learning Algorithms
- Category: Supervised Learning
- Category: Machine Learning
- Category: Data Processing
- Category: Predictive Modeling
- Category: Random Forest Algorithm
- Category: Predictive Analytics
- Category: Data Collection
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
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Instructors
Instructor ratings
4.5 (136 ratings)Jeff Leek, PhD
Johns Hopkins University
32 Courses1,723,948 learners
Offered by
Johns Hopkins University
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Learner reviews
4.5
3,257 reviews
- 5 stars
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22.32%
- 3 stars
6.93%
- 2 stars
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- 1 star
1.81%
Showing 3 of 3257
MR
5
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
CS
4
Reviewed on Feb 11, 2018
Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.
HP
5
Reviewed on Jan 15, 2017
It was like opening up a door to a whole new world. I have discovered new tools that I will thoroughly enjoy to use for the exploration of data and for predictions. Thanks Team Coursera !
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