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What are the Commonly Used Machine Learning Algorithms?

Knowledge Hut

There is no end to what can be achieved with the right ML algorithm. Machine Learning is comprised of different types of algorithms, each of which performs a unique task. U sers deploy these algorithms based on the problem statement and complexity of the problem they deal with.

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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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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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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.

Algorithm 160
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K-Nearest Neighbor (KNN) Algorithm for Machine Learning

Knowledge Hut

If you are thinking of a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classifications as well as regression problems, K-Nearest Neighbors (K-NN) is a perfect choice. K-Nearest Neighbors is one of the most basic supervised machine learning algorithms, yet very essential.

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How To Calculate Algorithm Efficiency

KDnuggets

In this article, we will discuss how to calculate algorithm efficiency, focusing on two main ways to measure it and providing an overview of the calculation process.

Algorithm 115
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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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