FeaturedUdemy
Dimensionality Reduction in R
4.8(97)2k studentsBEGINNEREnglish
Korshub's take
The Korshub Take
In an era of data overload, filtering essential signal from noise is a critical skill for building fast, interpretable predictive models. This beginner-friendly course teaches you how to implement dimensionality reduction in R using the modern tidymodels ecosystem, streamlining feature spaces without compromising predictive accuracy.
Who It's For
R programmers and data scientists who want to simplify complex feature sets and build lean, performant models using tidymodels.
Key Takeaways
- Feature Selection vs. Extraction: Understand when to filter out low-information features versus extracting condensed composite components.
- Tidymodels Integration: Use R's modern tidymodels framework to incorporate dimensionality reduction directly into machine learning workflows.
- Model Optimization: Speed up model training and reduce production latency by training on simplified, high-impact datasets.
About this course
Do you ever work with datasets with an overwhelming number of features? Do you need all those features? Which ones are the most important? In this course, you will learn dimensionality reduction techniques that will help you simplify your data and the models that you build with your data while maintaining the information in the original data and good predictive performance.
We live in the information age—an era of information overload. The art of extracting essential information from data is a marketable skill. Models train faster on reduced data. In production, smaller models mean faster response time. Perhaps most important, smaller data and models are often easier to understand. Dimensionality reduction is your Occam’s razor in data science.
The difference between feature selection and feature extraction! Using R, you will learn how to identify and remove features with low or redundant information, keeping the features with the most information. That’s feature selection. You will also learn how to extract combinations of features as condensed components that contain maximal information. That’s feature extraction!
But most importantly, using R’s new tidymodel package, you will use real-world data to build models with fewer features without sacrificing significant performance.
Why learn dimensionality reduction?
We live in the information age—an era of information overload. The art of extracting essential information from data is a marketable skill. Models train faster on reduced data. In production, smaller models mean faster response time. Perhaps most important, smaller data and models are often easier to understand. Dimensionality reduction is your Occam’s razor in data science.
What will you learn in this course?
The difference between feature selection and feature extraction! Using R, you will learn how to identify and remove features with low or redundant information, keeping the features with the most information. That’s feature selection. You will also learn how to extract combinations of features as condensed components that contain maximal information. That’s feature extraction!
But most importantly, using R’s new tidymodel package, you will use real-world data to build models with fewer features without sacrificing significant performance.
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