Performing Dimension Analysis with R

سرفصل های دوره

This course covers virtually all of the important techniques of dimensionality reduction available in R, allowing model builders to optimize model performance by reducing overfitting and saving on model training time and cost.

1. Course Overview

1. Course Overview

2. Understanding the Importance of Reducing Complexity in Data

01. Version Check

02. Prerequisites and Course Outline

03. The Curse of Dimensionality

04. Drawbacks of Dimensionality Reduction

05. Overfitting and the Bias-variance Trade-off

06. Techniques to Reduce Complexity

07. Choosing the Right Technique

08. Demo - Exploring the Boston Housing Dataset

09. Demo - Selecting Predictors to Build Regression Models

10. Demo - Performing Stepwise and Kitchen Sink Regression

3. Performing Dimensional Analysis for Continuous Data

1. Principal Components Analysis

2. Partial Least Squares Regression

3. Demo - Performing Principal Component Analysis

4. Demo - Regression Using Principal Components

5. Demo - Principal Components Regression

6. Demo - Partial Least Squares Regression

4. Performing Dimensional Analysis for Categorical Data

1. Linear Discriminant Analysis and Quadratic Discriminant Analysis

2. Demo - Exploring and Preparing Data for Linear Discriminant Analysis

3. Demo - Performing Linear Discriminant Analysis on Glass Data

4. Demo - Performing Quadratic Discriminant Analysis on Glass Data

5. Performing Dimensional Analysis for Non-linear Data

1. Manifold Learning

2. Demo - Manifold Learning Using Metric and Non-metric MDS

3. Demo - Manifold Learning Using t-SNE, Isomap, and LLE

4. Demo - Kernel PCA for Non-linearly Separable Data

5. Module Summary

189,000 تومان

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تولید کننده: PluralSight