MathWorks
Predictive Modeling and Machine Learning with MATLAB
MathWorks

Predictive Modeling and Machine Learning with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Taught in English

Some content may not be translated

Michael Reardon
Maria Gavilan-Alfonso
Erin Byrne

Instructors: Michael Reardon

15,323 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(112 reviews)

Beginner level
No prior experience required
22 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply a full machine learning workflow, from cleaning data to training & evaluating models using a real-world dataset

  • Use apps to quickly train many machine learning models to find the best approach for your application

  • Customize training using cost matrices to emphasize important classes

Details to know

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(112 reviews)

Beginner level
No prior experience required
22 hours (approximately)
Flexible schedule
Learn at your own pace

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

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Build your subject-matter expertise

This course is part of the Practical Data Science with MATLAB Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

In this module you'll apply the skills gained from the first two courses in the specialization on a new dataset. You'll be introduced to the Supervised Machine Learning Workflow and learn key terms. You'll end the module by creating and evaluating regression machine learning models.

What's included

11 videos8 readings3 quizzes4 app items1 discussion prompt

In this module you'll learn the basics of classification models. You'll train several types of classification models and evaluation the results.

What's included

6 videos7 readings2 quizzes1 discussion prompt

In this module you'll apply the complete supervised machine learning workflow. You'll use validation data inform model creation. You'll apply different feature selection techniques to reduce model complexity. You'll create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project.

What's included

10 videos5 readings2 quizzes1 peer review1 discussion prompt

What's included

5 videos7 readings2 quizzes1 discussion prompt

Instructors

Instructor ratings
4.9 (51 ratings)
Michael Reardon
MathWorks
4 Courses50,606 learners

Offered by

MathWorks

Recommended if you're interested in Machine Learning

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4.8

112 reviews

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FE
5

Reviewed on Oct 3, 2020

MP
5

Reviewed on Mar 20, 2021

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5

Reviewed on Jan 5, 2022

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