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⇱ Predictive Modeling and Machine Learning with MATLAB | Coursera

Predictive Modeling and Machine Learning with MATLAB

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Predictive Modeling and Machine Learning with MATLAB

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Gain insight into a topic and learn the fundamentals.
4.8

120 reviews

Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.8

120 reviews

Beginner level
No prior experience required
2 weeks to complete
at 10 hours a week
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

11 assignments¹

AI Graded see disclaimer
Taught in English

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
  • Earn a shareable career certificate

There are 4 modules in this course

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do.

These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.

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 assignments4 app items1 discussion prompt

11 videos•Total 73 minutes
  • Practical Data Science with MATLAB•4 minutes
  • Instructor Introduction•3 minutes
  • Introduction to Supervised Machine Learning•5 minutes
  • Introduction to the Taxi Data•8 minutes
  • Creating and Cleaning Features•9 minutes
  • Introduction to Regression•8 minutes
  • Using the Regression Learner App•11 minutes
  • Customizing Model Parameters•10 minutes
  • Evaluating Regression Models•7 minutes
  • Evaluate Your Model in MATLAB•9 minutes
  • Summary of Regression•2 minutes
8 readings•Total 85 minutes
  • Access MATLAB•15 minutes
  • Data and Code Files•15 minutes
  • Supervised Machine Learning Reference•10 minutes
  • Introduction to Module 1•5 minutes
  • Variables in the Taxi Data•10 minutes
  • Note regarding updates to MATLAB•5 minutes
  • Summary of Regression Models•15 minutes
  • Regression Metrics•10 minutes
3 assignments•Total 87 minutes
  • Feature Engineering Review•12 minutes
  • Train a Regression Model•30 minutes
  • Apply the Regression Workflow•45 minutes
4 app items•Total 60 minutes
  • Practice with Linear Regression Models•15 minutes
  • Practice with Regression trees•15 minutes
  • Practice Calculating R²•15 minutes
  • Practice Comparing Models•15 minutes
1 discussion prompt•Total 10 minutes
  • More Features, More Questions•10 minutes

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 assignments1 discussion prompt

6 videos•Total 45 minutes
  • Introduction to Classification•10 minutes
  • Using the Classification Learner App•8 minutes
  • Evaluating Classification Models•12 minutes
  • Evaluating Classification Models in MATLAB•6 minutes
  • Training a Multiclass Model•8 minutes
  • Summary of Classification•2 minutes
7 readings•Total 125 minutes
  • Introduction to Module 2•5 minutes
  • Note regarding updates to MATLAB•5 minutes
  • Summary of Classification Models•15 minutes
  • Binary Classification Metrics Reference•20 minutes
  • Evaluate and Customize Classification Models•30 minutes
  • Multiclass Classification Metrics Reference•20 minutes
  • Customizing Multiclass Models•30 minutes
2 assignments•Total 80 minutes
  • Train a Classification Model•30 minutes
  • Apply The Classification Workflow•50 minutes
1 discussion prompt•Total 15 minutes
  • Can you improve the model?•15 minutes

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 readings4 assignments1 discussion prompt

10 videos•Total 51 minutes
  • Addressing Underfitting and Overfitting•9 minutes
  • Using Validation Data During Training•4 minutes
  • Embedded Methods for Feature Selection•7 minutes
  • Using Regularization to Prevent Overfitting•6 minutes
  • Introduction to Ensemble Models•4 minutes
  • Training Ensemble Models•3 minutes
  • Introduction to Hyperparameters•5 minutes
  • Optimizing Hyperparameters•6 minutes
  • Evaluating and Using Your Model•5 minutes
  • Summary of Module 3•2 minutes
5 readings•Total 105 minutes
  • Introduction to Module 3•10 minutes
  • Examining Bias Variance Trade-off•15 minutes
  • Practice Partitioning Data•30 minutes
  • Using Wrapper Methods to Select Features•40 minutes
  • Setup for Mini-Project: Predicting Taxi Demand•10 minutes
4 assignments•Total 100 minutes
  • Practice Reducing Model Complexity•30 minutes
  • Applying Ensemble Models•30 minutes
  • The Supervised Machine Learning Workflow•10 minutes
  • Mini-Project: Predicting Taxi Demand•30 minutes
1 discussion prompt•Total 10 minutes
  • Share Your Model Results•10 minutes

What's included

5 videos7 readings2 assignments1 discussion prompt

5 videos•Total 26 minutes
  • Handling Class Imbalance•5 minutes
  • Reducing Specific Errors Using Cost Matrices•7 minutes
  • Integrating Your Model•3 minutes
  • A Discussion with Heather•7 minutes
  • Summary of Predictive Modeling and Machine Learning•3 minutes
7 readings•Total 160 minutes
  • Introduction to Module 4•5 minutes
  • Sampling Data•30 minutes
  • Practice Handling Class Imbalance•30 minutes
  • Oversampling the Minority Class•30 minutes
  • Examples of Integrating Machine Learning Models•15 minutes
  • Automated Machine Learning•45 minutes
  • Provide Feedback on Your Course Experience•5 minutes
2 assignments•Total 90 minutes
  • Practice Reducing Prediction Errors•30 minutes
  • Quiz: Advanced Topics and Next Steps•60 minutes
1 discussion prompt•Total 10 minutes
  • How will you use your models?•10 minutes

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Instructors

Instructor ratings
4.9 (54 ratings)
MathWorks
4 Courses•56,095 learners

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Showing 3 of 120

JD
·

Reviewed on Oct 25, 2020

Great Course and very helpful. Good to be able to put hands on real data and exercises.

SV
·

Reviewed on Jan 5, 2022

Thanks to Mathworks team for such a w​ell structured course with quality content and lectures. Looking forward to more such quality content such as deep learning and reinforced learning

AM
·

Reviewed on Nov 6, 2020

Outstanding course with real practical study case and easy to understand approach to build ML models and deploy it for production for end-user.Good job MathWorks.

Frequently asked questions

Yes. A free license to MATLAB Online is available to learners enrolled in the course. You can view the supported browsers here.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.