About this Course

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Intermediate Level
Approx. 17 hours to complete
English

Skills you will gain

Machine LearningMatlabPredictive Modelling
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 17 hours to complete
English

Offered by

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MathWorks

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Creating Regression Models

5 hours to complete
11 videos (Total 73 min), 7 readings, 7 quizzes
11 videos
Instructor Introduction2m
Introduction to Supervised Machine Learning4m
Introduction to the Taxi Data7m
Creating and Cleaning Features8m
Introduction to Regression8m
Using the Regression Learner App10m
Customizing Model Parameters9m
Evaluating Regression Models6m
Evaluate Your Model in MATLAB9m
Summary of Regression1m
7 readings
Download and Install MATLAB15m
Data and Code Files15m
Supervised Machine Learning Reference10m
Introduction to Module 15m
Variables in the Taxi Data10m
Summary of Regression Models15m
Regression Metrics10m
3 practice exercises
Feature Engineering Review12m
Train a Regression Model30m
Apply the Regression Workflow45m
Week
2

Week 2

4 hours to complete

Creating Classification Models

4 hours to complete
6 videos (Total 45 min), 6 readings, 2 quizzes
6 videos
Using the Classification Learner App7m
Evaluating Classification Models11m
Evaluating Classification Models in MATLAB5m
Training a Multiclass Model7m
Summary of Classification1m
6 readings
Introduction to Module 25m
Summary of Classification Models15m
Binary Classification Metrics Reference20m
Evaluate and Customize Classification Models30m
Multiclass Classification Metrics Reference20m
Customizing Multiclass Models30m
2 practice exercises
Train a Classification Model30m
Apply The Classification Workflow50m
Week
3

Week 3

8 hours to complete

Applying the Supervised Machine Learning Workflow

8 hours to complete
9 videos (Total 49 min), 5 readings, 3 quizzes
9 videos
Using Validation Data During Training3m
Embedded Methods for Feature Selection7m
Using Regularization to Prevent Overfitting6m
Introduction to Ensemble Models3m
Training Ensemble Models3m
Introduction to Hyperparameters5m
Optimizing Hyperparameters8m
Summary of Module 32m
5 readings
Introduction to Module 310m
Examining Bias Variance Trade-off15m
Practice Partitioning Data30m
Using Wrapper Methods to Select Features40m
Introduction to the Course Project10m
2 practice exercises
Practice Reducing Model Complexity30m
Applying Ensemble Models30m

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About the Practical Data Science with MATLAB Specialization

Practical Data Science with MATLAB

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