Predictive Modeling and Machine Learning with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Instructors: Michael Reardon

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 assignments

Taught in English

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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.

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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 videosTotal 73 minutes

Practical Data Science with MATLAB3 minutesPreview module
Instructor Introduction2 minutes
Introduction to Supervised Machine Learning4 minutes
Introduction to the Taxi Data7 minutes
Creating and Cleaning Features8 minutes
Introduction to Regression8 minutes
Using the Regression Learner App10 minutes
Customizing Model Parameters9 minutes
Evaluating Regression Models6 minutes
Evaluate Your Model in MATLAB9 minutes
Summary of Regression1 minute

8 readingsTotal 85 minutes

Access MATLAB15 minutes
Data and Code Files15 minutes
Supervised Machine Learning Reference10 minutes
Introduction to Module 15 minutes
Variables in the Taxi Data10 minutes
Note regarding updates to MATLAB5 minutes
Summary of Regression Models15 minutes
Regression Metrics10 minutes

3 assignmentsTotal 87 minutes

Feature Engineering Review12 minutes
Train a Regression Model30 minutes
Apply the Regression Workflow45 minutes

4 app itemsTotal 60 minutes

Practice with Linear Regression Models15 minutes
Practice with Regression trees15 minutes
Practice Calculating R²15 minutes
Practice Comparing Models15 minutes

1 discussion promptTotal 10 minutes

More Features, More Questions10 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 videosTotal 45 minutes

Introduction to Classification9 minutesPreview module
Using the Classification Learner App7 minutes
Evaluating Classification Models11 minutes
Evaluating Classification Models in MATLAB5 minutes
Training a Multiclass Model7 minutes
Summary of Classification1 minute

7 readingsTotal 125 minutes

Introduction to Module 25 minutes
Note regarding updates to MATLAB5 minutes
Summary of Classification Models15 minutes
Binary Classification Metrics Reference20 minutes
Evaluate and Customize Classification Models30 minutes
Multiclass Classification Metrics Reference20 minutes
Customizing Multiclass Models30 minutes

2 assignmentsTotal 80 minutes

Train a Classification Model30 minutes
Apply The Classification Workflow50 minutes

1 discussion promptTotal 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 videosTotal 51 minutes

Addressing Underfitting and Overfitting8 minutesPreview module
Using Validation Data During Training3 minutes
Embedded Methods for Feature Selection7 minutes
Using Regularization to Prevent Overfitting6 minutes
Introduction to Ensemble Models3 minutes
Training Ensemble Models3 minutes
Introduction to Hyperparameters5 minutes
Optimizing Hyperparameters5 minutes
Evaluating and Using Your Model4 minutes
Summary of Module 32 minutes

5 readingsTotal 105 minutes

Introduction to Module 310 minutes
Examining Bias Variance Trade-off15 minutes
Practice Partitioning Data30 minutes
Using Wrapper Methods to Select Features40 minutes
Setup for Mini-Project: Predicting Taxi Demand10 minutes

4 assignmentsTotal 100 minutes

Practice Reducing Model Complexity30 minutes
Applying Ensemble Models30 minutes
The Supervised Machine Learning Workflow10 minutes
Mini-Project: Predicting Taxi Demand30 minutes

1 discussion promptTotal 10 minutes

Share Your Model Results10 minutes

What's included

5 videos7 readings2 assignments1 discussion prompt

5 videosTotal 26 minutes

Handling Class Imbalance5 minutesPreview module
Reducing Specific Errors Using Cost Matrices7 minutes
Integrating Your Model3 minutes
A Discussion with Heather7 minutes
Summary of Predictive Modeling and Machine Learning3 minutes

7 readingsTotal 160 minutes

Introduction to Module 45 minutes
Sampling Data30 minutes
Practice Handling Class Imbalance30 minutes
Oversampling the Minority Class30 minutes
Examples of Integrating Machine Learning Models15 minutes
Automated Machine Learning45 minutes
Provide Feedback on Your Course Experience5 minutes

2 assignmentsTotal 90 minutes

Practice Reducing Prediction Errors30 minutes
Quiz: Advanced Topics and Next Steps60 minutes

1 discussion promptTotal 10 minutes

How will you use your models?10 minutes

Instructors

Instructor ratings

4.9 (53 ratings)

Michael Reardon

MathWorks

4 Courses52,320 learners

Offered by

MathWorks

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Reviewed on Sep 10, 2020

Very practical, but still high-level view to manage such projects. Testing was sufficient to test a full understanding. Thanks, I learnt a lot.

Reviewed on Mar 20, 2021

Helpful in defining data preprocessing and model creation using different mechanism

Reviewed on Jan 5, 2022

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

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