- DeepMind Wants to Reimagine One of the Most Important Algorithms in Machine Learning - May 14, 2021.
In one of the most important papers this year, DeepMind proposed a multi-agent structure to redefine PCA.
Algorithms, DeepMind, Game Theory, Machine Learning, PCA
- Matrix Decomposition Decoded - Dec 11, 2020.
This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.
Linear Algebra, Mathematics, numpy, PCA, Python
- 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.
Compression, Dimensionality Reduction, LDA, PCA, Python
- 20 Core Data Science Concepts for Beginners - Dec 8, 2020.
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
Beginners, Bias, Cross-validation, Data Science, Data Visualization, Data Wrangling, Outliers, PCA, Variance
- 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.
Dimensionality Reduction, numpy, PCA, Python
- 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!
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
- An Introduction to t-SNE with Python Example - Aug 15, 2018.
In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.
Clustering, Data Visualization, PCA, Python, t-SNE
- 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
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.
Classification, Dimensionality Reduction, Machine Learning, PCA, R
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
Pages: 1 2
Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM
- Top 10 Machine Learning with R Videos - Oct 24, 2017.
A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.
Algorithms, Clustering, K-nearest neighbors, Machine Learning, PCA, R, Text Mining, Top 10, Youtube
- Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
Pages: 1 2
Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10
- 5 Steps for Advanced Data Analysis using Visualization - Oct 28, 2016.
In most of the scientific researches, due to large amount of experiment data, statistical analysis is typically done by technical experts in computing and statistics. Unfortunately, these experts are not the experts of underlying research; which may cause gaps in analysis. If actual researchers are given easy to use tools and methods to handle and analyse data, it will enrich the research outcome for sure.
Bioinformatics, Clustering, Data Exploration, Data Visualization, Noise, PCA, Qlucore, Statistical Analysis
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization
- Nutrition & Principal Component Analysis: A Tutorial - Jun 16, 2016.
A great overview of Principal Component Analysis (PCA), with an example application in the field of nutrition.
Pages: 1 2
Algobeans, Feature Selection, Food, Nutrition, PCA
- A comparison between PCA and hierarchical clustering - Feb 23, 2016.
Graphical representations of high-dimensional data sets are the backbone of exploratory data analysis. We examine 2 of the most commonly used methods: heatmaps combined with hierarchical clustering and principal component analysis (PCA).
Clustering, Data Visualization, Life Science, PCA, Qlucore
- How to reduce Data Hoarding, get Better Visualizations and Decisions - May 21, 2015.
Creating a hodge-podge of pretty pictures of every datapoint is a guaranteed way to destroy the value of a visualization. We examine how to reduce such data hoarding and improve decisions.
Pages: 1 2 3
Alex Jones, Dashboard, Data Visualization, Linear Discriminant Analysis, PCA