Tag: Dimensionality Reduction (10)

• How to Prepare Your Data - Jun 30, 2020.
  
  This is an overview of structuring, cleaning, and enriching raw data.

  Tags: Data Preparation, Data Preprocessing, Dimensionality Reduction, Missing Values, Outliers

• 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.

  Tags: Dimensionality Reduction, numpy, PCA, Python

• 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.

  Tags: Data Preparation, Data Science, Dimensionality Reduction, Feature Engineering, Machine Learning

• 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!

  Tags: 7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning

• 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.

  Tags: Data Scientist, Dimensionality Reduction, Hiring, random forests algorithm, Venn Diagram

• 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.

  Tags: Data Science, Dimensionality Reduction, Linear Discriminant Analysis, Principal component analysis

• 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

  Tags: Data Science, Data Scientist, Deep Learning, Dimensionality Reduction, GDPR, Image Recognition, Machine Learning, PCA, Text Mining

• 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.

  Tags: Classification, Dimensionality Reduction, Machine Learning, PCA, R

• 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.

  Tags: Dimensionality Reduction, High-dimensional, Interview questions

• 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?

  Tags: Data Mining Training, Dimensionality Reduction, Ethics, Youtube