IBM
Supervised Machine Learning: Regression
IBM

Supervised Machine Learning: Regression

This course is part of multiple programs.

Taught in English

Some content may not be translated

Mark J Grover
Miguel Maldonado
Svitlana (Lana) Kramar

Instructors: Mark J Grover

36,172 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(542 reviews)

|

93%

Intermediate level
Some related experience required
20 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

13 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(542 reviews)

|

93%

Intermediate level
Some related experience required
20 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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 quizzes2 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 quizzes2 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 quizzes2 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 quizzes1 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 quizzes2 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 (170 ratings)
Mark J Grover
IBM
13 Courses91,719 learners
Miguel Maldonado
IBM
5 Courses67,449 learners
Svitlana (Lana) Kramar
IBM
3 Courses110,937 learners

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IBM

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4.7

542 reviews

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