This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Regression
Supervised Machine Learning: Regression
This course is part of multiple programs.
Instructors: Mark J Grover
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4.7
(635 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 20 hours
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Assessments
13 assignments
Taught in English
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There are 6 modules in this course
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
What's included
11 videos2 readings3 assignments2 app items
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
What's included
7 videos1 reading3 assignments2 app items
There is a trade-off between the size of your training set and your testing set. If you use most of your data for training, you will have fewer samples to validate your model. Conversely, if you use more samples for testing, you will have fewer samples to train your model. Cross Validation will allow you to reuse your data to use more samples for training and testing.
What's included
6 videos1 reading2 assignments2 app items1 plugin
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
What's included
10 videos1 reading3 assignments1 app item
In this section, you will understand the relationship between the loss function and the different regularization types.
What's included
5 videos1 reading2 assignments2 app items
In this section you will test everything you learned
What's included
1 reading1 peer review1 app item
Instructors
Instructor ratings
4.6 (191 ratings)Offered by
Recommended if you're interested in Machine Learning
Johns Hopkins University
Johns Hopkins University
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4.7
635 reviews
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Showing 3 of 635
GL
5
Reviewed on Jun 5, 2021
Please give the lecturer credit and include him as one of the instructors
PN
5
Reviewed on Feb 6, 2022
really good course, content is rich with good machine learning concepts
VO
5
Reviewed on Apr 9, 2021
Very well presented. This is without doubt the best series for Machine Learning on Coursera.
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