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Machine Learning Algorithms for Classification

KDnuggets

In this article, we will be going through the algorithms that can be used for classification tasks.

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A Roadmap to Machine Learning Algorithm Selection

KDnuggets

The goal of this article is to help demystify the process of selecting the proper machine learning algorithm, concentrating on "traditional" algorithms and offering some guidelines for choosing the best one for your application.

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Naïve Bayes Algorithm: Everything You Need to Know

KDnuggets

Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.

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Boosting Machine Learning Algorithms: An Overview

KDnuggets

The combination of several machine learning algorithms is referred to as ensemble learning. In this article, we will focus on boosting. There are several ensemble learning techniques.

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What is K-Means Clustering and How Does its Algorithm Work?

KDnuggets

In this article, we’ll cover what K-Means clustering is, how the algorithm works, choosing K, and a brief mention of its applications.

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Beyond Guesswork: Leveraging Bayesian Statistics for Effective Article Title Selection

KDnuggets

The article discusses how Bayesian multi-armed bandit algorithms can optimize digital media title selection, surpassing traditional A/B testing methods, demonstrated with a Python example, to boost audience engagement and decision-making in content creation.

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Linear Machine Learning Algorithms: An Overview

KDnuggets

In this article, we’ll discuss several linear algorithms and their concepts.

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