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  1. www.kaggle.com › dataFind Open Datasets and Machine Learning Projects | Kaggle

    Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.

  2. www.kaggle.comKaggle: Your Machine Learning and Data Science Community

    Level up with the largest AI & ML community. Join over 19 M+ machine learners to share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project. Register with Google.

  3. www.kaggle.com › codeRun Data Science & Machine Learning Code Online | Kaggle

    Explore and run machine learning code with Kaggle Notebooks. Find help in the Documentation.

  4. machinelearningmastery.com › using-kaggle-inUsing Kaggle in Machine Learning Projects

    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.

  5. www.analyticsvidhya.com › blog › 2021Kaggle Datasets | Top Kaggle Datasets to Practice on For Data ...

    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.

  6. www.datacamp.com › tutorial › kaggle-tutorialKaggle Tutorial: Your First ML Model | DataCamp

    Kaggle Tutorial: Your First Machine Learning Model Learn how to build your first machine learning model, a decision tree classifier, with the Python scikit-learn package, submit it to Kaggle and see how it performs!

  7. machinelearningmastery.com › get-started-with-kaggleHow to Get Started with Kaggle - Machine Learning Mastery

    Mar 10, 2017 · Overview. I recommend a simple 4-step process. The steps are: Pick a platform. Practice on standard datasets. Practice old Kaggle problems. Compete on Kaggle. The process is easy to describe, but difficult to implement. It is going to take time and effort. It is going to be hard work. But…

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