- Data Compression via Dimensionality Reduction: 3 Main Methods - Dec 10, 2020.
Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.
- How to Prepare Your Data - Jun 30, 2020.
This is an overview of structuring, cleaning, and enriching raw data.
- Dimensionality Reduction with Principal Component Analysis (PCA) - May 21, 2020.
This article focuses on design principles of the PCA algorithm for dimensionality reduction and its implementation in Python from scratch.
- Diffusion Map for Manifold Learning, Theory and Implementation - Mar 25, 2020.
This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions.
- 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
- KDnuggets™ News 19:n06, Feb 6: Data Scientists: Why are they so expensive to hire? An Essential Data Science Venn Diagram - Feb 6, 2019.
Also an overview of main methods for Dimension Reduction; an intuitive explanation of Random Forests®; how to avoid data visualization disasters; and trending Deep Learning Github repos.
- What Is Dimension Reduction In Data Science? - Jan 31, 2019.
An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.
- KDnuggets™ News 18:n27, Jul 18: Data Scientist was the sexiest job until…; Text Mining on the Command Line; Does PCA Really Work? - Jul 18, 2018.
Also: What is Minimum Viable (Data) Product?; Beating the 4-Year Slump: Mid-Career Growth in Data Science; GDPR after 2 months - What does it mean for Machine Learning?; Basic Image Data Analysis Using Numpy and OpenCV; fast.ai Deep Learning Part 2 Complete Course Notes
- Dimensionality Reduction : Does PCA really improve classification outcome? - Jul 13, 2018.
In this post, I am going to verify this statement using a Principal Component Analysis ( PCA ) to try to improve the classification performance of a neural network over a dataset.
- Must-Know: What is the curse of dimensionality? - Apr 18, 2017.
What is the curse of dimensionality? This post gives a no-nonsense overview of the concept, plain and simple.
- KDnuggets™ News 15:n16, May 20: 7 Techniques for Dimensionality Reduction; Who are the real Data Scientists? - May 20, 2015.
Seven Techniques for Data Dimensionality Reduction; Will the Real Data Scientists Please Stand Up. Most Viewed Data Mining Videos on YouTube; Should Data Science Really Do That?