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MATLAB: Data Classification with MATLAB
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Advanced Provider : Gabriel Popoola 3 Lessons · 21m in Arabic, German, English, Spanish, French, Portuguese, Chinese
Course Description
Data classification models are the foundation of various systems in defense and industry. There are a large number of factors that can contribute to an increase or decrease in model performance and reliability. Before diving into the training and selection of your classification model, classification is the process of mapping a set of inputs to a discrete output variable. It is essential that you have an excellent foundational understanding of just what classification is as well as the workflow associated with selecting the best model for your application and needs. The goal is to get the machine learning model to learn a mapping function given a set of class-labeled input data inputs. In this MATLAB module, you will learn about data classification and how to implement it in MATLAB.
After completing this module, you can apply what you learned to any project or task involving data classification. This will enable you to create and implement different predictive models. These can be used in applications for things such as finance, advertising, weather/climate, and even medicine.
What You'll Learn
- Define what data classification is and the process of mapping inputs to a discrete output variable
- Recognize the concepts of inputs and outputs in data classification
- Understand the classification model workflow
- Implement data classification in MATLAB
- Select the best classification model for a given application and needs
- Create and implement different predictive models for applications such as finance, advertising, weather/climate, and medicine
Key Takeaways
- Classification is the process of mapping a set of inputs to a discrete output variable.
- Data classification models are the foundation of various systems in defense and industry.
- The goal of classification is to get the machine learning model to learn a mapping function given class-labeled input data.
- A strong foundational understanding of classification and the model-selection workflow is essential before training and selecting a classification model.
- Data classification can be applied to predictive models in fields such as finance, advertising, weather/climate, and medicine.
Frequently Asked Questions
What will I learn in this MATLAB module?
You will learn what data classification is and how to implement it in MATLAB, including the concepts of inputs and outputs and the classification model workflow.
What topics does this course cover?
The course covers a classification overview, classification model selection, and the data classification model workflow.
What can I do after completing this module?
After completing this module, you can apply what you learned to any project or task involving data classification, enabling you to create and implement different predictive models for applications such as finance, advertising, weather/climate, and medicine.
What skills does this course help develop?
This course helps develop skills in Data Classification, Data Science, MATLAB, MeshLab, and Statistical Classification.
How is data classification defined in this course?
Classification is defined as the process of mapping a set of inputs to a discrete output variable, with the goal of having a machine learning model learn a mapping function from class-labeled input data.
