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Automate document validation and fraud detection in the mortgage underwriting process using AWS AI services: Part 1

AWS Machine Learning Blog

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Source: Equifax) Part 1 of this series discusses the most common challenges associated with the manual lending process.

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

Flipboard

Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season. To avoid leakage during cross-validation, we grouped all plays from the same game into the same fold. He works with AWS customers to solve business problems with artificial intelligence and machine learning.

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

The integration with Amazon Bedrock is achieved through the Boto3 Python module, which serves as an interface to the AWS, enabling seamless interaction with Amazon Bedrock and the deployment of the classification model. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

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How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines

AWS Machine Learning Blog

Were using Bayesian optimization for hyperparameter tuning and cross-validation to reduce overfitting. One benefit of this step is the ability to use built-in algorithms for common data transformations and automatic scaling of resources. This helps make sure that the clustering is accurate and relevant. amazonaws.com/{2}:{3}".format(account_id,

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MLOps: A complete guide for building, deploying, and managing machine learning models

Data Science Dojo

MLOps emphasizes the need for continuous integration and continuous deployment (CI/CD) in the ML workflow, ensuring that models are updated in real-time to reflect changes in data or ML algorithms. Examples include: Cross-validation techniques for better model evaluation.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Below, we explore some of the most widely used algorithms in ML.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.