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Practicing Machine Learning with Imbalanced Dataset

Analytics Vidhya

Introduction In today’s world, machine learning and artificial intelligence are widely used in almost every sector to improve performance and results. The machine learning algorithms heavily rely on data that we feed to them. But are they still useful without the data? The answer is No.

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Decision Tree Algorithm in Machine Learning: Types, Examples

Knowledge Hut

Machine Learning is an interdisciplinary field of study and is a sub-domain of Artificial Intelligence. It gives computers the ability to learn and infer from a huge amount of homogeneous data, without having to be programmed explicitly. For this, we will take up an example dataset called the “PlayTennis” dataset.

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How to get datasets for Machine Learning?

Knowledge Hut

Datasets are the repository of information that is required to solve a particular type of problem. Datasets play a crucial role and are at the heart of all Machine Learning models. Machine learning uses algorithms that comb through data sets and continuously improve the machine learning model.

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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. Machine Learning is among the fastest evolving trends in the I T industry.

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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. If you explore machine learning with Python syllabus , you will realize the extent of the application of KNN.

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The Role of Mathematics in Machine Learning

Knowledge Hut

Automation and machine learning have changed our lives. From self – driving Cars, robots, image recognition, diagnostic assessments, recommendation engines, Photo Tagging, fraud detection and more, the future for machine learning and AI is bright and full of untapped possibilities. Why do I need to Learn Math?

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An Easy Guide to Choose the Right Machine Learning Algorithm

KDnuggets

There's no free lunch in machine learning. So, determining which algorithm to use depends on many factors from the type of problem at hand to the type of output you are looking for. This guide offers several considerations to review when exploring the right ML approach for your dataset.