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Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science

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Machine Learning

Description

Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves

Features

Features are nothing but the independent variables in machine learning models. What is required to be learned in any specific machine learning problem is a set of these features (independent variables), coefficients of these features, and parameters for coming up with appropriate functions or models (also termed hyperparameters). The following represents a few examples of what can be termed as features of machine learning models:

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Pandas NumPy Pandas NumPy

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AndroidX Refactored versions of the Android APIs that are not bundled with the operating system.
AndroidX Test Includes APIs for testing your Android app, including Espresso, JUnit Runner, JUnit4 rules, and UI Automator.

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Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science

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