Machine Learning with Python - IDE3 algorithm

The IDE3 algorithm in Machine Learning with Python

The IDE3 (Iterative Dichotomiser 3) algorithm is a predecessor of the C4.5 algorithm and represents one of the first algorithms for building decision trees. Even though C4.5 and its successors have become more popular, IDE3 is still interesting because it helped lay the foundation for decision trees and machine learning. Below, I will explain how IDE3 works and how to use it in Python.

Machine Learning with Python - Beyond regression and classification problems

Machine Learning: beyond classification and regression problems

t Machine Learning is a very broad field, and there are many other types of problems and techniques beyond classification and regression. Problems such as clustering, dimensionality reduction, reinforcement learning, text generation, and many others are equally important and present unique challenges. Many advanced courses and more specialized resources also cover these less popular topics. So, if you have an interest in specific types of Machine Learning problems, you can find specialized resources to meet your needs.

Machine Learning with Python - Entropy and Information Gain

Entropy and information gain in Machine Learning

In machine learning, entropy and information gain are fundamental concepts used in decision trees and supervised learning to make data division decisions during the training process of a model. These concepts are often associated with the Iterative Dichotomiser 3 (ID3) algorithm and its variants, such as

Machine Learning with Python - CHAID h

The CHAID algorithm in Machine Learning with Python

CHAID (Chi-squared Automatic Interaction Detector) is an algorithm used for building decision trees, in particular for splitting variables based on their interactions with target variables. Unlike traditional decision trees, which rely primarily on the Gini index or entropy to choose splits, CHAID uses chi-square tests to automatically determine optimal splits.

Machine Learning with Python - Gradient Boosting

Gradient Boosting in Machine Learning with Python

The Gradient Boosting algorithm is a machine learning technique that builds a predictive model by combining several weaker models (usually decision trees) together into a single strong structure. The main goal of Gradient Boosting is to progressively improve the weaknesses of weak models, allowing you to create a stronger and more adaptable model.

Deep Learning

Deep Learning

Deep learning is a computational technique that allows you to extract and transform data from sources such as human speech or image classification, using multiple layers of neural networks. Each of these layers takes its inputs from the previous layers and refines them, so progressively. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way the networks learn to perform specific tasks.