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Janani Ravi

Reducing Complexity in Data

IT & Software

Self-paced course

Certification program

Deep Learning

Computer Science

Pluralsight

3 Hours

Free trial

English

Advanced

Video format

Course Overview

Version Check

Module Overview

Prerequisites and Course Outline

The Curse of Dimensionality

Overfitting and the Bias-variance Trade-off

Techniques to Reduce Complexity

Choosing the Right Technique

Drawbacks of Reducing Complexity

Demo: The Diabetes Dataset - Exploration

Demo: Establishing a Baseline Model

Demo: The Boston Housing Prices Dataset - Exploration

Demo: Kitchen Sink Regression to Establish a Baseline Model

Summary

Understanding the Need for Dimensionality Reduction

Statistical Techniques for Feature Selection

Conceptual Overview of Different Feature Selection Techniques

Demo: Selecting Features Using a Variance Threshold

Demo: Selecting K Best Features Using Chi2 Analysis

Demo: Setting up Helper Functions for Feature Selection

Demo: Find the Right Value for K Using Chi2 Analysis

Demo: Find the Right Value for K Using ANOVA

Demo: Select Features Using Percentiles and Mutual Information Analysis

Demo: Dictionary Learning on Handwritten Digits

Using Statistical Techniques for Feature Selection

Understanding Principal Components Analysis

Demo: Performing PCA to Reduce Dimensionality

Demo: Building Linear Models Using Principal Components

Understanding Factor Analysis

Demo: Applying Factor Analysis to Reduce Dimensionality

Understanding Linear Discriminant Analysis

Demo: Performing Linear Discriminant Analysis to Reorient Data

Reducing Complexity in Linear Data

Understanding Manifold Learning

Demo: Generate Manifold and Set up Helper Functions

Demo: Manifold Learning Using Multidimensional Scaling and Spectral Embedding

Demo: Manifold Learning Using t-SNE and Isomap

Demo: Manifold Learning Using Locally Linear Embedding

Demo: Performing Kernel PCA to Reduce Complexity in Nonlinear Data

Reducing Complexity in Nonlinear Data

K-means Model Stacking

Demo: Classifying Image with Original Features

Demo: Transforming Data Using K-means Cluster Centers

Autoencoding

Demo: Prepare Image Data to Feed an Autoencoder

Demo: Using Autoencoders to Learn Efficient Representations of Data

Summary and Further Study

Dimensionality Reduction Using Clustering and Autoencoding Techniques

Reducing Complexity in Data

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Provided by

Pluralsight

Taught by

Janani Ravi

Reducing Complexity in Data