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Dimensionality Reduction in Python
4.8(878)36k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
High-dimensional datasets introduce complexity, noise, and computational bottlenecks in machine learning pipelines. This intermediate Python course equips you with specialized visualization, feature selection, and feature extraction techniques to eliminate redundant variables and extract key signals without losing crucial information.
Who It's For
Data analysts and machine learning practitioners in Python who want to streamline high-dimensional data, reduce model overfitting, and speed up training.
Key Takeaways
- High-Dimensional Visualization: Apply specialized visualization methods to explore data structures containing numerous dimensions.
- Feature Selection: Detect and drop low-variance, duplicate, or irrelevant features that contribute no predictive power.
- Feature Extraction: Calculate uncorrelated principal components (PCA) to compress datasets while retaining maximal variance.
About this course
High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.
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