This course is part of the Practical Data Science with MATLAB Specialization

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stars
103 ratings

Michael Reardon +10 more instructors

13,160 already enrolled

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Practical Data Science with MATLAB SpecializationMathWorks

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

Practical Data Science with MATLAB Specialization

Intermediate Level

Approx. 22 hours to complete

English

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Skills you will gain

Machine Learning
Matlab
Predictive Modelling

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 4 in the

Practical Data Science with MATLAB Specialization

Intermediate Level

Approx. 22 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.84/5 (47 Ratings)

Offered by

MathWorks

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Syllabus - What you will learn from this course

Week1

Week 1

5 hours to complete

Creating Regression Models

5 hours to complete

11 videos (Total 73 min), 8 readings, 7 quizzes

11 videos

Practical Data Science with MATLAB3mInstructor Introduction2mIntroduction to Supervised Machine Learning4mIntroduction to the Taxi Data7mCreating and Cleaning Features8mIntroduction to Regression8mUsing the Regression Learner App10mCustomizing Model Parameters9mEvaluating Regression Models6mEvaluate Your Model in MATLAB9mSummary of Regression1m

8 readings

Access MATLAB15mData and Code Files15mSupervised Machine Learning Reference10mIntroduction to Module 15mVariables in the Taxi Data10mNote regarding updates to MATLAB5mSummary of Regression Models15mRegression Metrics10m

3 practice exercises

Feature Engineering Review12mTrain a Regression Model30mApply the Regression Workflow45m

Week2

Week 2

4 hours to complete

Creating Classification Models

4 hours to complete

6 videos (Total 45 min), 7 readings, 2 quizzes

6 videos

Introduction to Classification9mUsing the Classification Learner App7mEvaluating Classification Models11mEvaluating Classification Models in MATLAB5mTraining a Multiclass Model7mSummary of Classification1m

7 readings

Introduction to Module 25mNote regarding updates to MATLAB5mSummary of Classification Models15mBinary Classification Metrics Reference20mEvaluate and Customize Classification Models30mMulticlass Classification Metrics Reference20mCustomizing Multiclass Models30m

2 practice exercises

Train a Classification Model30mApply The Classification Workflow50m

Week3

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

Addressing Underfitting and Overfitting8mUsing Validation Data During Training3mEmbedded Methods for Feature Selection7mUsing Regularization to Prevent Overfitting6mIntroduction to Ensemble Models3mTraining Ensemble Models3mIntroduction to Hyperparameters5mOptimizing Hyperparameters8mSummary of Module 32m

5 readings

Introduction to Module 310mExamining Bias Variance Trade-off15mPractice Partitioning Data30mUsing Wrapper Methods to Select Features40mIntroduction to the Course Project10m

2 practice exercises

Practice Reducing Model Complexity30mApplying Ensemble Models30m

Week4

Week 4

5 hours to complete

Advanced Topics and Next Steps

5 hours to complete

5 videos (Total 26 min), 7 readings, 2 quizzes

5 videos

Handling Class Imbalance5mReducing Specific Errors Using Cost Matrices7mIntegrating Your Model3mA Discussion with Heather7mSummary of Predictive Modeling and Machine Learning3m

7 readings

Introduction to Module 45mSampling Data30mPractice Handling Class Imbalance30mOversampling the Minority Class30mExamples of Integrating Machine Learning Models15mAutomated Machine Learning45mProvide Feedback on Your Course Experience5m

2 practice exercises

Practice Reducing Prediction Errors30mQuiz: Advanced Topics and Next Steps1h

Reviews

4.8

26 reviews

5 stars
79.80%
4 stars
18.26%
3 stars
1.92%

TOP REVIEWS FROM PREDICTIVE MODELING AND MACHINE LEARNING WITH MATLAB

by MPMar 20, 2021

Helpful in defining data preprocessing and model creation using different mechanism

by JDOct 25, 2020

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

by ESMay 15, 2021

Great course. It's well-organized, well-designed and quite useful for learning machine learning workflow and algorithms with practical approaches.

by ACDec 13, 2020

Great course, I like how they keep everything organized and pretty straightforward.

View all reviews

About the Practical Data Science with MATLAB Specialization

Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?

Completing this specialization will give you the skills and confidence you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare, to the auto industry, to tech startups. This specialization assumes you have domain expertise in a technical field and some exposure to computational tools, such as spreadsheets. To be successful in completing the courses, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation). Throughout this specialization, you will be using MATLAB. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your data science tasks. You will be provided with free access to MATLAB for the duration of the specialization to complete your work.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
What will I get if I subscribe to this Specialization?
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
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.
Will I have access to MATLAB?
Yes. A free license to MATLAB Online is available to learners enrolled in the course. You can view the supported browsers here.

More questions? Visit the Learner Help Center.

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Predictive Modeling and Machine Learning with MATLAB

About this Course

Skills you will gain

Instructors

Michael Reardon

Maria Gavilan-Alfonso

Erin Byrne

Matt Rich

Brandon Armstrong

Adam Filion

Isaac Bruss

Nikola Trica

Brian Buechel

Heather Gorr

Sam Jones

Offered by

MathWorks

Syllabus - What you will learn from this course

Creating Regression Models

Creating Classification Models

Applying the Supervised Machine Learning Workflow

Advanced Topics and Next Steps

Reviews

TOP REVIEWS FROM PREDICTIVE MODELING AND MACHINE LEARNING WITH MATLAB

About the Practical Data Science with MATLAB Specialization

Frequently Asked Questions

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Predictive Modeling and Machine Learning with MATLAB

About this Course

Skills you will gain

Instructors

Michael Reardon

Maria Gavilan-Alfonso

Erin Byrne

Matt Rich

Brandon Armstrong

Adam Filion

Isaac Bruss

Nikola Trica

Brian Buechel

Heather Gorr

Sam Jones

Offered by

MathWorks

Syllabus - What you will learn from this course

Creating Regression Models

Creating Classification Models

Applying the Supervised Machine Learning Workflow

Advanced Topics and Next Steps

Reviews

TOP REVIEWS FROM PREDICTIVE MODELING AND MACHINE LEARNING WITH MATLAB

About the Practical Data Science with MATLAB Specialization

Frequently Asked Questions

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