Description
Dimensionality Reduction in R, the training course Dimensionality Reduction in R is published by Datacamp Academy. Have you ever worked with datasets with a large number of features? Do you need all these features? Which of them is more important? In this course, you will learn dimensionality reduction techniques that help you simplify your data and the models you build with your data while preserving the information in the original data and improving performance. Have a good vision. We live in the information age. The art of extracting essential information from data is a lucrative skill. Models are trained faster on reduced data. In production, smaller models mean faster response times. Perhaps the most important of them is the understanding of smaller data and models. Dimensionality reduction is your winning blade in data science. The difference between feature selection and feature extraction Using R, you will learn how to identify and remove features with little or redundant information and keep features with the most information. This is a feature selection. You will also learn how to extract combinations of features as dense components that contain maximum information.
What you will learn
- Basics of dimension reduction
- Feature selection for feature importance
- Feature selection for model performance
- Feature extraction and model performance
Dimensionality Reduction in R course specifications
- Publisher: Datacamp
- teacher : Matt Pickard
- English language
- Education level: all levels
- Number of courses: 4
- Training duration: 4 hours to complete the course
Head of chapters of Dimensionality Reduction in R course
Course prerequisites
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
90 MB
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