Unsupervised learning: seeking representations of the data
Clustering: grouping observations together
The problem solved in clustering
Given the iris...
Model selection: choosing estimators and their parameters
Score, and cross-validated scores
As we have seen, every estimator exposes a score method that can judge...
Supervised learning: predicting an output variable from high-dimensional observations
The problem solved in supervised learning
Supervised learning consists in learning the link between two datasets:...
A tutorial on statistical-learning for scientific data processing
Statistical learning
Machine learning is a technique with a growing importance, as...
How to Develop an Intuition for Probability With Worked Examples
Probability calculations are frustratingly unintuitive.
Our brains are too eager to take shortcuts and get the wrong answer, instead of thinking through a problem and...
How to Develop an Intuition for Joint, Marginal, and Conditional Probability
Probability for a single random variable is straight forward, although it can become complicated when considering two or more variables.
With just two variables, we...
A Gentle Introduction to Joint, Marginal, and Conditional Probability
Probability quantifies the uncertainty of the outcomes of a random variable.
It is relatively easy to understand and compute the probability for a single variable....
A Gentle Introduction to Probability Density Estimation
Last Updated on September 25, 2019
Probability density is the relationship between observations and their probability.
Some outcomes of a random variable will have low probability...
Python List Comprehensions
Share
Katie SilverioMarch 18, 2019
Machine learning scikit-learn package
8.1. Getting started with scikit-learn
This is one of the 100+ free recipes...