**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.

Tags: 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

Tags: 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.

Tags: 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!

Tags: Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, Random Forests, 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

Tags: 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).

Tags: 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

Tags: Alex Jones, Dashboard, Data Visualization, Linear Discriminant Analysis, PCA