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What are the Commonly Used Machine Learning Algorithms?

Knowledge Hut

Machine Learning is a sub-branch of Artificial Intelligence, used for the analysis of data. It learns from the data that is input and predicts the output from the data rather than being explicitly programmed. There is no end to what can be achieved with the right ML algorithm.

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Decision Tree Algorithm in Machine Learning: Types, Examples

Knowledge Hut

It gives computers the ability to learn and infer from a huge amount of homogeneous data, without having to be programmed explicitly. In a more generalized form, Entropy is given by the following equation: Example: As an example, a sample S is taken, which contains 14 data samples and includes 9 positive and 5 negative samples.

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Time Complexity: Significance, Types, Algorithms

Knowledge Hut

” In this article, we are going to discuss time complexity of algorithms and how they are significant to us. We would want to make use of data structures in organizing and storing data in a way so that it can be accessed and manipulated efficiently. Then, check out these Programming courses.

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7 Machine Learning Algorithms You Can’t Miss

KDnuggets

This list of machine learning algorithms is a good place to start your journey as a data scientist. You should be able to identify the most common models and use them in the right applications.

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A Beginner’s Guide to the Top 10 Machine Learning Algorithms

KDnuggets

Data science’s essence lies in machine learning algorithms. Here are ten algorithms that are a great introduction to machine learning for any beginner!

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Popular Machine Learning Algorithms

KDnuggets

This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.

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Bring Your Own Algorithm to Anomaly Detection

Pinterest Engineering

It aims to be fast, scalable, and end-to-end: starting from fetching the data from various data sources to be analyzed, and ending with pushing result notifications to tools like Slack. They found the existing selection of anomaly detection algorithms in EGADs to be limiting. What’s the Goal?