Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors

Finding Machine Learning Datasets

Photo of author

By Team EasyExamNotes

1. Google Dataset Search: Google Dataset Search is a valuable tool for researchers, data scientists, and enthusiasts in the field of machine learning and data analysis. This service, accessible at https://datasetsearch.research.google.com/, is designed to simplify the process of discovering datasets from a wide range of domains.

2. Kaggle: Kaggle is a well-known platform that hosts data science competitions and many other datasets. Find datasets in different domains and participate in competitions utilizing your machine learning skills.

3. UCI Machine Learning Repository: The University of California, Irvine maintains a repository where you can get datasets for machine learning projects. These are available for free and cover a wide range of topics.

4. Government and NGO Websites: Social, economic, and environmental issues are often covered by government institutions and NGOs datasets; examples include data.gov, World Bank Data or UNICEF Data.

5. Open Data Platforms: Some countries and cities have open data platforms where they share information related to public services (e.g., transportation), health among others. Seek out open data initiatives in your area.

6. GitHub: Many researchers and organizations use GitHub to share their dataset collections. You may look for repositories with datasets or go directly into repositories specializing in machine learning datasets.

7. Machine Learning Competitions Platforms: Some other platforms that run machine learning contests besides Kaggle are DrivenData or CrowdANALYTIX where you can also get the respective competition’s dataset.

8. Academic Databases: Academic institutions often provide research-related data sets. Check university websites, especially those relating to data science and ML departments.

9. Social Media APIs: APIs from social media sites such as Twitter, Facebook and Instagram enable access to public data feeds. Note that there terms of service as well as privacy regulations guiding how social media data should be used should be followed strictly.

10. Synthetic Data Generators: In some cases synthetic data generators can be used to create artificial datasets for testing purposes or experimentation only with regards to this matter.

Download as PDF

Related posts:

  1. Define machine learning and explain its importance in real-world applications.
  2. Differences Between Machine Learning and Artificial Intelligence
  3. Machine Learning works on which type of data ?
  4. What is Regression in Machine learning
  5. What is hypothesis function and testing
  6. Explain computer vision with an appropriate example
  7. Explain Reinformcement learning with an appropriate exaple
  8. Reinforcement Learning Framework
  9. Data augmentation
  10. Normalizing Data Sets in Machine Learning
  11. Machine learning models
  12. Unsupervised machine learning
  13. Neural Network in Machine Learning
  14. Recurrent neural network
  15. Support Vector Machines
  16. Long short-term memory (LSTM) networks
  17. Convolutional neural network
  18. How to implement Convolutional neural network in Python
  19. What does it mean to train a model on a dataset ?
  20. Can a textual dataset be used with an openCV?
  21. Name some popular machine learning libraries.
  22. Introduction to Machine Learning
  23. Like machine learning, what are other approaches in AI ?
  24. What is Machine learning ?
  25. What is Machine Learning ?
  26. Types of Machine Learning ?
  27. Applications of Machine Learning
  28. Data Preprocessing
  29. Data Cleaning
  30. Handling Missing Data
  31. Feature Scaling
  32. Labeled data in Machine learning
  33. Difference between Supervised vs Unsupervised vs Reinforcement learning
  34. Machine learning algorithms for Big data
  35. Difference between Supervised vs Unsupervised vs Reinforcement learning
  36. What is training data in Machine learning
  37. What is Ordinary Least Squares (OLS) estimation
  38. Scalar in Machine Learning
  39. Scalars in Loss Functions | Machine Learning
  40. Linear Algebra for Machine Learning Practitioners
  41. Supervised Learning
  42. Top Interview Questions and Answers for Supervised Learning
  43. What are the different types of machine learning?
  44. What is a hyperparameter in machine learning ?
  45. Unsupervised Learning Interview Q&A
  46. TOP INTERVIEW QUESTIONS AND ANSWERS FOR Artificial Intelligence
  47. Deep Learning Top Interview Questions and Answers
  48. What is target variable and independent variable in machine learning
  49. Machine Learning Scope and Limitations
  50. Statistics and linear algebra for machine learning
  51. What is MNIST ?
  52. Some real time examples of machine learning