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How to get datasets for Machine Learning?

Knowledge Hut

Datasets are the repository of information that is required to solve a particular type of problem. Datasets play a crucial role and are at the heart of all Machine Learning models. Datasets are often related to a particular type of problem and machine learning models can be built to solve those problems by learning from the data.

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Beyond Garbage Collection: Tackling the Challenge of Orphaned Datasets

Ascend.io

A prime example of such patterns is orphaned datasets. These are datasets that exist in a database or data storage system but no longer have a relevant link or relationship to other data, to any of the analytics, or to the main application — making them a deceptively challenging issue to tackle.

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Medical Datasets for Machine Learning: Aims, Types and Common Use Cases

AltexSoft

In this post, we’ll briefly discuss challenges you face when working with medical data and make an overview of publucly available healthcare datasets, along with practical tasks they help solve. Read our article on HIPAA violations to avoid common mistakes and associated penalties. Medical datasets comparison chart .

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Top 10 Data Science Websites to learn More

Knowledge Hut

Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. Hypothesis testing is a part of inferential statistics which uses data from a sample to analyze results about whole dataset or population. While using Amazon SageMaker datasets are quick to access and load.

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Data News — Week 24.14

Christophe Blefari

How we built Text-to-SQL at Pinterest — Pinterest open-sourced a tool called Querybook that they used to access Pinterest data every day. This article greatly explained how they did it. They deeply explain in this article why they choose ClickHouse to monitor their ClickHouse Cloud offering saving money on their Datadog bill.

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Data News — Week 23.15

Christophe Blefari

Using Metrics Layer to standardize and scale experimentation at DoorDash — A very good exhaustive article about a metrics layer. Mainly they define measures, dimensions and metrics in YAML that will be materialised and made accessible to Curie (their experimentation platform). That’s why they built this system.

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Simplifying BI pipelines with Snowflake dynamic tables

ThoughtSpot

These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot. This article provides a technical overview of this integration so you can make the most of your data investment. Set refresh schedules as needed.

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