MathWorks
Data Processing and Feature Engineering with MATLAB
MathWorks

Data Processing and Feature Engineering with MATLAB

This course is part of Practical Data Science with MATLAB Specialization

Taught in English

Some content may not be translated

Michael Reardon
Maria Gavilan-Alfonso
Erin Byrne

Instructors: Michael Reardon

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Course

Gain insight into a topic and learn the fundamentals

4.7

(331 reviews)

|

96%

Intermediate level
Some related experience required
20 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Prepare data for further analysis by removing noise, identifying outliers, & merging data from multiple sources

  • Create and evaluate features for machine learning applications

  • Explore special techniques for handling textual, audio, & image data

  • Perform unsupervised machine learning

Details to know

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Assessments

11 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(331 reviews)

|

96%

Intermediate level
Some related experience required
20 hours (approximately)
Flexible schedule
Learn at your own pace

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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 5 modules in this course

In this module you'll apply the skills gained in Exploratory Data Analysis with MATLAB on a new dataset. You'll explore different types of distributions and calculate quantities like the skewness and interquartile range. You'll also learn about more types of plots for visualizing multi-dimensional data.

What's included

10 videos4 readings2 quizzes1 discussion prompt

In this module you'll learn to prepare data for analysis. Often data is not recorded as required. You'll learn to manipulate string variables to extract key information. You'll create a single datetime variable from date and time information spread across multiple columns in a table. You'll efficiently load and combine data from multiple files to create a final table for analysis.

What's included

8 videos2 readings2 quizzes1 app item1 discussion prompt

In this module you'll clean messy data. Missing data, outliers, and variables with very different scales can obscure trends in the data. You'll find and address missing data and outliers in a data set. You'll compare variables with different scales by normalizing variables.

What's included

9 videos2 readings2 quizzes1 app item

In this module you'll create new features to better understand your data. You'll evaluate features to determine if a feature is potentially useful for making predictions.

What's included

7 videos5 readings1 quiz1 app item1 discussion prompt

In this module you'll apply the concepts from Modules 1 through 4 to different domains. You'll create and evaluate features using time-based signals such as accelerometer data from a cell phone. You'll use Apps in MATLAB to perform image processing and create features based on segmented images. You'll also use text processing techniques to find features in unstructured text.

What's included

11 videos5 readings4 quizzes1 app item1 discussion prompt

Instructors

Instructor ratings
4.7 (102 ratings)
Michael Reardon
MathWorks
4 Courses49,650 learners
Maria Gavilan-Alfonso
MathWorks
4 Courses49,650 learners

Offered by

MathWorks

Recommended if you're interested in Data Analysis

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4.7

331 reviews

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5

Reviewed on Apr 15, 2020

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Reviewed on Feb 20, 2021

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Reviewed on Oct 16, 2020

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