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Alternative Feature Selection Methods in Machine Learning

KDnuggets

In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score. Feature selection methodologies go beyond filter, wrapper and embedded methods.

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KDnuggets Top Posts for June 2022: 21 Cheat Sheets for Data Science Interviews

KDnuggets

14 Essential Git Commands for Data Scientists • Statistics and Probability for Data Science • 20 Basic Linux Commands for Data Science Beginners • 3 Ways Understanding Bayes Theorem Will Improve Your Data Science • Learn MLOps with This Free Course • Primary Supervised Learning Algorithms Used in Machine LearningData Preparation with SQL Cheatsheet. (..)

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Classification and Regression using AutoKeras

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction on AutoKeras Automated Machine Learning (AutoML) is a computerised way of determining the best combination of data preparation, model, and hyperparameters for a predictive modelling task.

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Why Machine Learning has Become a Key Tool in Dynamic Pricing

Dataconomy

With the most recent developments in machine learning , this process has become more accurate, flexible, and fast: algorithms analyze vast amounts of data, glean insights from the data, and find optimal solutions. Analysts built price elasticity models based on price, discounts, and customer behavior.

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others.

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Machine Learning with MATLAB and Amazon SageMaker

Flipboard

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. Because we have a model of the system and faults are rare in operation, we can take advantage of simulated data to train our algorithm.

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Synthetic data

Dataconomy

By creating artificial datasets that mimic real-world statistics without compromising personal information, organizations can harness the power of data while adhering to stringent privacy regulations. What is synthetic data? Historical context The use of synthetic data has evolved significantly since its inception in the 1990s.