I liked the course “Intro to Machine Learning” by Udacity. This image summarizes what it was about:

# Posts published in “Shallow Machine Learning”

In order to avoid overfitting in regression due to too many feature while at the same time have enough features to minimize the sum of squared errors in order to…

To detect and get rid of outliers in a dataset (which may for instance have been caused by sensor error or data entry error) you first train your data, and…

Two slightly similar concepts in supervised machine learning are Supervised classification, and regression. With supervised classification you will get a discrete output (a label or boolean value) and in regression…

One of the simples algorithms in Machine Learning is k-Neares Neighbors. It is considered a “lazy learning” algorithm where all the calculations are deferred until classification. It works like this:…

When you have to deal with non-linear decision making, you can use decision trees to transform it into a linear decision surface. Let say we have a buddy that goes…

A Support Vector Machine is an algorithm that outputs a line separating two classes in a set of data. An SVM will try to maximise the margin from the line…

Naive Bayes Classifier is a probabilistic classifier used in supervised machine learning that is especially useful when categorizing texts. They apply Bayes’ Theorem which describes the probability of an event to take…