The Ultimate Guide to Data Sets for Machine Learning

Disclaimer: This content is provided for informational purposes only and does not intend to substitute financial, educational, health, nutritional, medical, legal, etc advice provided by a professional.

The Ultimate Guide to Data Sets for Machine Learning

Welcome to the ultimate guide on data sets for machine learning! If you're interested in diving into the world of machine learning, one of the first things you'll need is a high-quality data set. In this comprehensive guide, we'll explore a variety of data sets that are perfect for machine learning projects. Whether you're a beginner or an advanced practitioner, there's something here for everyone.

Contents:

  • List of sorting used for datasets
  • List of open data portals
  • List of portals suitable for multiple types of applications
  • List of portals suitable for a specific subtype of applications
  • Image data
  • Text data
  • Sound data
  • Signal data
  • Physical data
  • Biological data
  • Anomaly data
  • Question answering data
  • Dialog or instruction prompted data
  • Cybersecurity
  • Climate and sustainability
  • Code data
  • Multivariate data
  • Curated repositories of datasets
  • See also
  • References

List of sorting used for datasets

When it comes to data sets for machine learning, sorting plays a crucial role. Sorting allows you to organize and categorize data based on specific criteria. Here are some common sorting methods used in machine learning:

  • Alphabetical sorting
  • Chronological sorting
  • Numerical sorting
  • Relevance sorting
  • Popularity sorting
  • Complex sorting

List of open data portals

Open data portals are platforms that provide free and open access to data sets. These portals are a treasure trove of valuable data that can be used for machine learning projects. Here are some popular open data portals:

  • OpenML
  • Kaggle
  • UCI Machine Learning Repository
  • Google Public Data
  • Data.gov
  • World Bank Open Data

List of portals suitable for multiple types of applications

Some data portals are specifically designed to cater to multiple types of applications. These portals offer a wide range of data sets that can be used across various domains. Here are a few portals suitable for multiple types of applications:

  • Data.gov
  • Kaggle
  • Google Public Data
  • World Bank Open Data
  • UCI Machine Learning Repository

List of portals suitable for a specific subtype of applications

There are also data portals that are tailored for specific subtypes of applications. These portals focus on providing data sets that are relevant to a particular domain or industry. Here are some examples of portals suitable for a specific subtype of applications:

  • Climate Data Online (CDO)
  • GenBank
  • PhysioNet
  • Human Genome Project

Image data

Image data sets are widely used in machine learning for tasks like image recognition, object detection, and image generation. Here are some popular image data sets:

  • MNIST
  • CIFAR-10
  • ImageNet
  • PASCAL VOC
  • Open Images Dataset

Text data

Text data sets are commonly used in natural language processing (NLP) tasks such as sentiment analysis, text classification, and machine translation. Here are a few text data sets:

  • IMDB Movie Review
  • Twitter Sentiment Analysis
  • 20 Newsgroups
  • Amazon Reviews
  • Wikipedia Text

Sound data

Sound data sets are used for various audio-related machine learning tasks, including speech recognition, music classification, and sound event detection. Here are some popular sound data sets:

  • UrbanSound
  • ESC-50
  • Freesound
  • NSynth
  • MagnaTagATune

Signal data

Signal data sets are used in applications such as signal processing, audio analysis, and time series forecasting. Here are a few signal data sets:

  • ECG Heartbeat
  • EEG Brainwave
  • Stock Market Data
  • Weather Data
  • Electricity Consumption Data

Physical data

Physical data sets include data related to various physical phenomena and processes. These data sets are used in fields such as physics, astronomy, and earth science. Here are a few examples of physical data sets:

  • Kepler Exoplanet
  • Hubble Space Telescope
  • Global Climate Data
  • Seismic Data
  • Volcano Eruption Data

Biological data

Biological data sets encompass data related to living organisms and their biological processes. These data sets are used in fields such as genomics, proteomics, and bioinformatics. Here are a few biological data sets:

  • Human Genome
  • Protein Data Bank
  • Gene Expression Omnibus
  • DrugBank
  • Ensembl

Anomaly data

Anomaly data sets are used to detect and classify anomalies or outliers in a given data set. These data sets are particularly useful in anomaly detection tasks for various domains. Here are a few anomaly data sets:

  • KDD Cup 1999
  • Thyroid Disease
  • Credit Card Fraud
  • Intrusion Detection
  • Numenta Anomaly Benchmark

Question answering data

Question answering data sets are used to train machine learning models that can accurately answer questions based on a given context or knowledge base. Here are a few question answering data sets:

  • SQuAD
  • TriviaQA
  • MS MARCO
  • WikiQA
  • SearchQA

Dialog or instruction prompted data

Dialog or instruction prompted data sets are used for tasks like chatbot training, dialogue generation, and instruction following. Here are a few dialog or instruction prompted data sets:

  • Persona-Chat
  • Twitter Dialogue
  • Taskmaster
  • CoQA
  • InstructGPT

Cybersecurity

Cybersecurity data sets are used to analyze and detect various cyber threats, vulnerabilities, and attacks. Here are a few cybersecurity data sets:

  • UNSW-NB15
  • KDD Cup 1999
  • Malware
  • DARPA Intrusion Detection
  • Botnet Traffic

Climate and sustainability

Climate and sustainability data sets provide valuable insights into climate patterns, environmental factors, and sustainable development. Here are a few climate and sustainability data sets:

  • Global Climate Data
  • Climate Data Online (CDO)
  • World Bank Climate Change Data
  • European Environment Agency Data
  • National Oceanic and Atmospheric Administration (NOAA) Data

Code data

Code data sets are used to analyze and understand programming languages, code quality, and software development practices. Here are a few code data sets:

  • Github Archive
  • Stack Overflow Data Dump
  • CodeSearchNet
  • Defects4J
  • CodeReview

Multivariate data

Multivariate data sets contain multiple variables or features that can be used to train machine learning models. These data sets are used in various multivariate analysis tasks. Here are a few multivariate data sets:

  • Boston Housing
  • UCI Adult
  • Iris
  • Wine Quality
  • Bank Marketing

Curated repositories of datasets

Curated repositories of data sets are platforms that provide a collection of high-quality data sets from various sources. These repositories are a great resource for finding reliable and well-documented data sets. Here are a few curated repositories of data sets:

  • OpenML
  • Kaggle Datasets
  • UCI Machine Learning Repository
  • Data.gov
  • Google Public Data

See also

For further exploration, check out these additional resources:

  • OpenML - Open platform for sharing datasets, algorithms, and experiments
  • 365 Data Science - Best Public Datasets for Machine Learning in 2024

References

For more information, refer to these references:

  • Wikipedia - List of datasets for machine-learning research

Conclusion

There you have it - the ultimate guide to data sets for machine learning. We've covered a wide range of data sets that are perfect for any machine learning project. Whether you're interested in image data, text data, sound data, signal data, physical data, biological data, anomaly data, question answering data, dialog or instruction prompted data, cybersecurity, climate and sustainability, code data, multivariate data, or curated repositories of datasets, this guide has something for everyone.

Remember, the quality and relevance of your data set are crucial for the success of your machine learning project. So take the time to explore the various data sets mentioned in this guide, and find the perfect data set for your specific needs.

Happy machine learning!

Disclaimer: This content is provided for informational purposes only and does not intend to substitute financial, educational, health, nutritional, medical, legal, etc advice provided by a professional.