Technology

Association rule learning is a technique used to uncover relationships or associations between items in large datasets, particularly used in market basket analysis. It aims to identify rules that describe how certain items often appear together. Two of the most popular algorithms for association rule learning are Apriori and Eclat. Apriori Algorithm: Principle: If an…

Clustering is an unsupervised machine learning method used to group similar instances (data points) into clusters without having prior labels for these groups. The idea is that data in one cluster is more similar to each other than to those in other clusters. Let’s delve into two of the most popular clustering algorithms: k-Means and…

The k-Nearest Neighbors (k-NN) algorithm is a simple, intuitive, and versatile supervised learning method used for classification and regression. The core idea behind k-NN is that similar data points (based on a certain distance measure) will have similar outcomes. How k-NN Works: Strengths of k-NN: Limitations of k-NN: Practical Considerations: In summary, k-NN is a…

Decision Trees Decision Trees are a popular machine learning algorithm used for both classification and regression tasks. They are a non-parametric supervised learning method. Basic Structure: How Decision Trees Work: Algorithm to Build a Decision Tree (like CART – Classification and Regression Trees): Strengths: Limitations: Random Forests Random Forests is an ensemble learning method that…

Neural networks, particularly artificial neural networks (ANNs), are a foundational element of modern artificial intelligence and machine learning. They are algorithms inspired by the structure and function of the brain’s biological neural networks. The primary aim of a neural network is to recognize patterns, which makes them particularly effective for tasks such as classification, regression,…

Support Vector Machines (SVM) is a supervised machine learning algorithm used primarily for classification tasks, though it can also be used for regression. It’s known for its ability to handle high-dimensional data and its efficacy in cases where the number of dimensions exceeds the number of samples. Basic Idea: SVM works by finding the hyperplane…