Data Processing and Feature Engineering with MATLAB
Master data preparation and feature engineering in MATLAB: clean, transform, and optimize data for machine learning applications.
Master data preparation and feature engineering in MATLAB: clean, transform, and optimize data for machine learning applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Practical Data Science with MATLAB Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
(339 ratings)
15,192 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
Powered by
What you'll learn
Prepare and clean data by handling missing values and outliers
Create and evaluate features for machine learning applications
Process text, audio, and image data effectively
Perform unsupervised learning for feature selection
Merge and synchronize data from multiple sources
Skills you'll gain
This course includes:
3.8 Hours PreRecorded video
11 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 5 modules in this course
This intermediate-level course builds on Exploratory Data Analysis skills to prepare data for predictive modeling. Students learn to handle missing data, remove noise, identify outliers, and merge data from multiple sources. The curriculum covers feature engineering techniques for various data types including text, audio, and images. Through hands-on exercises, learners develop skills in data cleaning, normalization, and feature evaluation for machine learning applications.
Surveying Your Data
Module 1 · 3 Hours to complete
Organizing Your Data
Module 2 · 3 Hours to complete
Cleaning Your Data
Module 3 · 3 Hours to complete
Finding Features that Matter
Module 4 · 4 Hours to complete
Domain-Specific Feature Engineering
Module 5 · 5 Hours to complete
Fee Structure
Instructors
Principal Online Course Developer at MathWorks
Nikola Trica holds a Dipl.-Ing. degree in Computer and Systems Engineering from Technische Universität Ilmenau, specializing in Systems Engineering and Intelligent Control. She has been with MathWorks since 2006, initially delivering training on MATLAB, Simulink, and Control Design to customers in Europe. Since 2014, she has transitioned to developing online content for global audiences.
Manager Online Courses
Brandon Armstrong is a Principal Online Content Developer at MathWorks. He earned a Ph.D. in physics from the University of California at Santa Barbara in 2010.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.7 course rating
339 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.