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Predictive Modeling and Machine Learning with MATLAB

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Predictive Modeling and Machine Learning with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Instructors: Michael Reardon

17,367 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

120 reviews

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments¹

AI Graded see disclaimer

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.

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.

Module details

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 MATLAB4 minutes
Instructor Introduction3 minutes
Introduction to Supervised Machine Learning5 minutes
Introduction to the Taxi Data8 minutes
Creating and Cleaning Features9 minutes
Introduction to Regression8 minutes
Using the Regression Learner App11 minutes
Customizing Model Parameters10 minutes
Evaluating Regression Models7 minutes
Evaluate Your Model in MATLAB9 minutes
Summary of Regression2 minutes

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

Apply the Regression Workflow45 minutes
Feature Engineering Review12 minutes
Train a Regression Model30 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 Classification10 minutes
Using the Classification Learner App8 minutes
Evaluating Classification Models12 minutes
Evaluating Classification Models in MATLAB6 minutes
Training a Multiclass Model8 minutes
Summary of Classification2 minutes

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

Apply The Classification Workflow50 minutes
Train a Classification Model30 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 Overfitting9 minutes
Using Validation Data During Training4 minutes
Embedded Methods for Feature Selection7 minutes
Using Regularization to Prevent Overfitting6 minutes
Introduction to Ensemble Models4 minutes
Training Ensemble Models3 minutes
Introduction to Hyperparameters5 minutes
Optimizing Hyperparameters6 minutes
Evaluating and Using Your Model5 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

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

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

1 discussion promptTotal 10 minutes

How will you use your models?10 minutes

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Instructors

Instructor ratings

(54 ratings)

Michael Reardon

MathWorks

4 Courses56,010 learners

Offered by

MathWorks

Learner reviews

5 stars
80.99%
4 stars
15.70%
3 stars
2.47%
2 stars
0%
1 star
0.82%

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Reviewed on Oct 25, 2020

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

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.

Reviewed on Sep 16, 2020

As always, a fast-paced course with crystal clear explanations!

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