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Justin Flett
MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of performing feature engineering within MATLAB.
Read more
MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of performing feature engineering within MATLAB.
Read more
MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of performing feature engineering within MATLAB.
MATLAB is a widely used programming language for statistical computing. In this course, Performing Feature Engineering with MATLAB, you will learn foundational knowledge for understanding and implementing data science and feature engineering problems with MATLAB. First, you will learn what feature engineering is and why it can be useful to us. Next, you will learn how to organize and clean raw data within MATLAB. Then, you will learn how to preprocess text and image data with MATLAB. Finally, you will learn how extract features for analysis within MATLAB. When you are finished with this course, you will have the skills and knowledge of data science and feature engineering basics with MATLAB.
Register for this course and see more details by visiting:
OpenCourser.com/course/vggyfo/performing
What's inside
Syllabus
Course Overview
Introducing and Understanding Feature Engineering
Organizing and Cleaning Data with MATLAB
Preprocessing Data with MATLAB
Read more
Syllabus
Course Overview
Introducing and Understanding Feature Engineering
Organizing and Cleaning Data with MATLAB
Preprocessing Data with MATLAB
Read more
Extracting Features for Analysis
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on the practical uses of MATLAB within industry
Suitable for both beginners and learners with foundational knowledge in data science
Covers data science and feature engineering comprehensively, providing a strong foundation
Teaches MATLAB, which is a widely used programming language within the field
Provides hands-on experience through coding labs and exercises
Instructed by Justin Flett, who is recognized for their work in the field
Save this course
Save Performing Feature Engineering with MATLAB to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Performing Feature Engineering with MATLAB with these
activities:
Review linear algebra
Show steps
Strengthen your foundation in linear algebra to enhance your understanding of data science concepts.
Show steps
-
Refer to your previous notes or教科书s.
-
Practice solving linear algebra problems.
-
Review online resources or take a refresher course.
Review MATLAB documentation
Show steps
Familiarize yourself with the capabilities of MATLAB through its documentation.
Show steps
-
Visit the official MATLAB documentation website.
-
Explore the different sections and functions.
-
Search for specific topics or functions.
Participate in a study group
Show steps
Collaborate with peers to discuss concepts, solve problems, and reinforce your learning.
Show steps
-
Find a study group with peers taking the same course.
-
Meet regularly to discuss the course material.
-
Work together on assignments and projects.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve coding exercises
Show steps
Reinforce your understanding of MATLAB syntax and data science concepts by solving coding exercises.
Show steps
-
Find coding exercises online.
-
Solve the exercises using MATLAB.
-
Review the solutions and understand the concepts.
Build a data science toolkit
Show steps
Organize and compile useful resources, code snippets, and datasets to support your data science journey.
Show steps
-
Gather resources from online sources.
-
Create a repository to store your toolkit.
-
Categorize and organize the resources.
Create a MATLAB function
Show steps
Deepen your understanding of MATLAB by creating your own function to perform data manipulation or analysis.
Show steps
-
Define the purpose and functionality of your function.
-
Write the code for your function.
-
Test the function and make necessary adjustments.
Analyze a real-world dataset
Show steps
Apply your data science knowledge and feature engineering skills to a practical problem.
Show steps
-
Choose a dataset that aligns with your interests.
-
Explore the data and identify patterns.
-
Extract and engineer features from the data.
-
Build and evaluate a model using your engineered features.
Review linear algebra
Show steps
Strengthen your foundation in linear algebra to enhance your understanding of data science concepts.
Show steps
Show steps
Show steps
- Refer to your previous notes or教科书s.
- Practice solving linear algebra problems.
- Review online resources or take a refresher course.
Review MATLAB documentation
Show steps
Familiarize yourself with the capabilities of MATLAB through its documentation.
Show steps
Show steps
Show steps
- Visit the official MATLAB documentation website.
- Explore the different sections and functions.
- Search for specific topics or functions.
Participate in a study group
Show steps
Collaborate with peers to discuss concepts, solve problems, and reinforce your learning.
Show steps
Show steps
Show steps
- Find a study group with peers taking the same course.
- Meet regularly to discuss the course material.
- Work together on assignments and projects.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve coding exercises
Show steps
Reinforce your understanding of MATLAB syntax and data science concepts by solving coding exercises.
Show steps
Show steps
Show steps
- Find coding exercises online.
- Solve the exercises using MATLAB.
- Review the solutions and understand the concepts.
Build a data science toolkit
Show steps
Organize and compile useful resources, code snippets, and datasets to support your data science journey.
Show steps
Show steps
Show steps
- Gather resources from online sources.
- Create a repository to store your toolkit.
- Categorize and organize the resources.
Create a MATLAB function
Show steps
Deepen your understanding of MATLAB by creating your own function to perform data manipulation or analysis.
Show steps
Show steps
Show steps
- Define the purpose and functionality of your function.
- Write the code for your function.
- Test the function and make necessary adjustments.
Analyze a real-world dataset
Show steps
Apply your data science knowledge and feature engineering skills to a practical problem.
Show steps
Show steps
Show steps
- Choose a dataset that aligns with your interests.
- Explore the data and identify patterns.
- Extract and engineer features from the data.
- Build and evaluate a model using your engineered features.
Career center
Learners who complete Performing Feature Engineering with MATLAB will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use programming and statistical skills to make sense out of large amounts of data. They apply feature engineering and other techniques from data science to guide business decisions. Performing Feature Engineering with MATLAB is foundational because it teaches learners how to preprocess and understand data with MATLAB. This is a key skill that Data Scientists use every day.
Data Analyst
Data Analyst
Data Analysts help businesses understand their data and make better decisions. They use feature engineering and statistical analysis to uncover trends and patterns in data. The skills learned in this MATLAB course can help build a foundation for success in Data Analytics.
Software Engineer
Software Engineer
Software Engineers build and maintain the software and applications that we use every day. Feature engineering is an important part of software development, as it helps to ensure that data is used efficiently and effectively. This course can help Software Engineers gain the skills they need to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models, which are used to make predictions based on data. Feature engineering is an important part of machine learning, as it helps to improve the accuracy of models. This course can help Machine Learning Engineers gain the skills they need to create powerful models.
Statistician
Statistician
Statisticians use data to solve real-world problems. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Statistics.
Data Architect
Data Architect
Data Architects design and manage data systems. In this role, one would use feature engineering to improve the efficiency and effectiveness of data systems. Taking a course in feature engineering with MATLAB may be useful for success in this field.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. These pipelines take data in and transform it for use by analysts and scientists. Feature engineering can help create more efficient pipelines. Taking a course in this subject may be useful for success in this field.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Business Analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Quantitative Analysis.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Financial Analysis.
Actuary
Actuary
Actuaries use data to assess risk and uncertainty. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Actuarial Science.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve business problems. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Operations Research.
Market Researcher
Market Researcher
Market Researchers use data to understand customer needs and behavior. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Market Research.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the causes of disease. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Epidemiology.
Biostatistician
Biostatistician
Biostatisticians use data to solve problems in biology and medicine. They use feature engineering and other statistical techniques to analyze data and draw conclusions. This course can help build a foundation for success in Biostatistics.
For more career information including salaries, visit:
OpenCourser.com/course/vggyfo/performing
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Performing Feature Engineering with MATLAB.
Save
Comprehensive guide to deep learning. It covers all the essential concepts, from neural networks to convolutional neural networks. It's a great resource for anyone who wants to learn more about deep learning.
Comprehensive guide to machine learning with R. It covers all the essential concepts, from data preprocessing to model evaluation. It's a great resource for anyone who wants to learn more about machine learning with R.
Comprehensive guide to feature engineering with Python. It covers all the essential concepts, from data cleaning and preprocessing to feature selection and transformation. It's a great resource for anyone who wants to learn more about feature engineering.
Comprehensive guide to machine learning with Python. It covers all the essential concepts, from data preprocessing to model evaluation. It's a great resource for anyone who wants to learn more about machine learning with Python.
Comprehensive guide to MATLAB programming for engineers and scientists. It covers all the essential concepts, from basic data types to advanced topics like object-oriented programming. It's a great resource for anyone who wants to learn more about MATLAB.
Comprehensive guide to data science and machine learning with MATLAB. It covers all the essential concepts, from data preprocessing to model evaluation. It's a great resource for anyone who wants to learn more about data science and machine learning.
Save
Gentle introduction to MATLAB for beginners. It covers all the essential concepts, from basic data types to advanced topics like object-oriented programming. It's a great resource for anyone who wants to learn more about MATLAB.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vggyfo/performing
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Time
54 hours
Level
Intermediate
Via
Pluralsight
Instructor
Justin Flett
Language
English
Good to know
Focuses on the practical uses of MATLAB within industry
Suitable for both beginners and learners with foundational knowledge in data science
Covers data science and feature engineering comprehensively, providing a strong foundation
Teaches MATLAB, which is a widely used programming language within the field
Provides hands-on experience through coding labs and exercises
Instructed by Justin Flett, who is recognized for their work in the field
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