AutoML (Automated Machine Learning) algorithms

AutoML (Automated Machine Learning) refers to the use of automated methods to automate the end-to-end process of applying machine learning to real-world problems. It involves automating tasks such as data pre-processing, feature engineering, model selection, hyperparameter tuning, and deployment. AutoML algorithms are designed to help data scientists and machine learning practitioners save time and effort in the machine learning pipeline by automating some of the most time-consuming tasks. The goal of AutoML is to enable people with little or no machine learning expertise to build and deploy machine learning models without requiring extensive knowledge of the underlying algorithms and technologies.

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