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Kaggle
Apr 21, 2021 · Working with Datasets on Kaggle is very easy and convenient and all beginners must try Kaggle, so as to build up some skill and knowledge.
Jan 10, 2022 · Kaggle is a website where you can find competitions to solve data science problems. It’s free to join and it gives you the opportunity to practice your skills on real-world datasets in various industries.
Jun 21, 2022 · For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours.
Kaggle is a data science platform but it also supports dataset handling. "Kaggle Datasets" allows you to create your own custom datasets, share them with others and easily import them into your notebooks. Additionally, you can add private datasets which would only be visible to you.
Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Jan 6, 2022 · In this blog post I want to give a brief overview of the official Kaggle API and its Python implementation. This API enables us to download any dataset from Kaggle using only Python. Once we have accomplished that we can then load the dataset with HF’s dataset library. The notebook for this tutorial can be found in this Github repo.
Nov 22, 2020 · Kaggle is one of the world’s largest community of data scientists and machine learning specialists. This platform is home to more than 1 million registered users, it has thousands of public datasets and code snippets (a.k.a. notebooks), more importantly, this platform is actively used by some of the world’s best data scientists.
Mar 8, 2017 · Kaggle is an online community platform for data scientists and machine learning enthusiasts. Kaggle allows users to collaborate with other users, find and publish datasets, use GPU integrated notebooks, and compete with other data scientists to solve data science challenges.
