Machine learning models need a large enough training dataset to produce insightful and detailed insights efficiently and effectively. As a result, open-source datasets assist in lowering potential data barriers in ML model training.
Let’s explore the top ten open-source datasets for machine learning research.
Appen Datasets Resource Center
High-quality licensable datasets from the Appen Datasets Resource Center offer enough information to train ML models. In addition, an extensive collection of “Off-the-Shelf ” open-source datasets is available, including more than 11,000 hours of audio, more than 25,000 images, and 8.7 million words in 80 different languages. With its high performance in AI algorithms, this open-source dataset offers to improve the accuracy of machine learning models. Its main goal is to satisfy the demands of the large global customer base that requests data for ML model training.
AWS
AWS or Amazon Web Services offers numerous open-source datasets in various fields, including public transportation, satellite imagery, and many more, which are helpful for machine learning models. Developers also have access to a search box where they can look for the appropriate datasets and look up specifics like the description and use of the dataset. These millions of datasets are in Amazon S3 and other AWS resources. This cloud service helps transfer datasets as quickly as possible and gain access to data for ML model training.
Azure Open Datasets
Microsoft Azure Open Datasets are quickly becoming well-liked open-source datasets with the aid of AI algorithms and machine learning. Azure incorporates features from curated datasets into various machine learning models to reduce the extra time needed for data preparation. As a result, developers and data scientists can deliver insights at scale by using Microsoft Azure Open Datasets and its data analytics solutions.
Big Bad NLP Database
The Big Bad NLP Database website displays the data description, format, and use case for machine learning. However, this website focuses solely on tasks involving natural language processing. In addition, some of the datasets are JSON format, necessitating some preprocessing before reading the data frame. Typically, real-world data comes in various forms.
Bureau of Transportation Statistics
The Bureau of Transportation Statistics is a good starting point if you want to learn more about demand forecasting. You can see how traveller habits have changed over time thanks to the combination of long-term trends and recent COVID-19-related Transportation Statistics. Furthermore, the Bureau of Transportation Statistics is also real-world information, so you’ll learn to navigate complex datasets.
Google Dataset Search
Google Dataset Search is one of the best open-source datasets for machine learning model training with AI algorithms. There are about 25 million datasets there that can train ML models effectively and efficiently. Developers and programmers can use a single keyword to search through thousands of online repositories looking for open-source datasets. In addition, it aids in developing a data-sharing ecosystem for data used to train machine learning and AI algorithms.
Kaggle
Kaggle is a massive repository of datasets. Text, audio, numerical, and image data are all available here. Because it facilitates the discovery and publication of data sets, new datasets will appear frequently. Kaggle is well-known among data scientists because of the competitions it hosts. Many ongoing competitions offer real prizes for participants.
PLOS
The Public Library of Science is an alternative to the for-profit scientific journals that dominate the research world. It also developed PLOS Open Data, a collection of open datasets usually related to the research published in the journal. So if you have a question about the analysis or want to rerun the numbers, the data will likely be available. Furthermore, PLOS is a critical opportunity for scientists to conduct meta-analysis by combining data from multiple studies to look for larger patterns and issues.
UCI Machine Learning Repo
UCI Machine Learning Repo is an excellent site for beginners. Researchers also use datasets from this site as examples in their lectures. There are 559 datasets, most of which are small and easy to understand. You can narrow down the datasets by telling it which machine learning algorithm (classification, clustering, regression, etc.) you want to use. Also, since many people have worked with these datasets before, you can look up what they did and get ideas from them.
VisualData
The visual data set contains more than 500 datasets. We can filter the dataset according to its popularity. Practising on some of the most well-known datasets is good if you’re starting deep learning to learn from your more experienced peers.
Source: indiaai.gov.in
