Machine Learning: Reinforcement Learning — Markov Decision Processes

The goal of reinforcement learning, contrary to the previously seen methods of machine learning (supervised/unsupervised learning), is to tweak the system in order to perform right or wrong in certain ways.

Machine Learning: Unsupervised Learning — Feature selection

Having a lot of features to deal with is not necessarily a good thing to have.

Instead we need to always aim to identify a subset of features that are more relevant than others and that are responsible…

Machine Learning: Neural Network

Another machine learning model is the “neural network”. It was inspired by how the real neurons interact with each other and it simplifies their activity in this form: we have different inputs x with different weights (w); the sum of the inputs by their weights triggers a…

Machine Learning: Unsupervised Learning — Principal Component Analysis

The Principal Component Analysis (PCA) is a method for feature selection that turns a set of correlated variables into the underlying set of orthogonal (latent) variables, that are not measured directly but…

Machine Learning — Supervised Learning Algorithms: Summary

This is a simple list of strengths and weaknesses of different classifiers common to the supervised learning algorithms.

LOGISTIC REGRESSION:

Machine Learning: Causes of error

In machine learning, results of an algorithm implementation are affected by errors. There are two main causes of errors that we need to analyze in order to improve our algorithm:

• bias
• variance

Machine Learning: Unsupervised Learning — Clustering

Unlike supervised learning, where we were dealing with labeled datasets, in unsupervised learning we have to learn a concept based on unlabeled data.

Machine Learning: Decision Tree Classifier

A decision tree classifier lets you make non-linear decisions, using simple linear questions.