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Data mining

Dataconomy

By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights. The data mining process The data mining process is structured into four primary stages: data gathering, data preparation, data mining, and data analysis and interpretation.

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Predictive modeling

Dataconomy

By identifying patterns within the data, it helps organizations anticipate trends or events, making it a vital component of predictive analytics. Definition and overview of predictive modeling At its core, predictive modeling involves creating a model using historical data that can predict future events.

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Machine learning algorithms

Dataconomy

Machine learning algorithms are specialized computational models designed to analyze data, recognize patterns, and make informed predictions or decisions. They leverage statistical techniques to enable machines to learn from previous experiences, refining their approaches as they encounter new data.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

It helps business owners and decision-makers choose the right technique based on the type of data they have and the outcome they want to achieve. Let us now look at the key differences starting with their definitions and the type of data they use.

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Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment. Data preparation The foundation of any machine learning project is data preparation.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Data Preparation for AI Projects Data preparation is critical in any AI project, laying the foundation for accurate and reliable model outcomes.

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How Light & Wonder built a predictive maintenance solution for gaming machines on AWS

AWS Machine Learning Blog

Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering. Data preparation To extract data efficiently for training and testing, we utilize Amazon Athena and the AWS Glue Data Catalog.

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