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20+ Datasets for ML (Machine learning) in 2024

Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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20+ Datasets for ML (Machine learning) in 202420+ Datasets for ML (Machine learning) in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Business leaders are ramping up their efforts towards implementing AI-powered solutions such as generative AI and conversational AI in their businesses to not fall behind the competition.

However, AI and machine learning projects can fail due to various reasons, with poor datasets being one of them. Selecting the right datasets for ML projects is one of the most crucial steps that must be done right. Whether you are working with an AI data collection service provider or preparing your own dataset, it is essential to know which datasets are required.

This article covers all you need to know about machine learning datasets and how you can choose the right one to start your project. It also provides a list of the top datasets and sources of datasets for machine learning model training.v

Here is a list of all the datasets and sources of AI/ML training datasets mentioned in this article.

Table 1. ML datasets and data sources

Domain / Source of DatasetDataset NameDescriptionFree / Paid
Custom human-generated datasetsClickworkerFreshly collected/generated data via a 4.5+ million crowdPaid
Custom human-generated datasetsAppenFreshly collected/generated data via a 1+ million crowdPaid
Custom human-generated datasetsAmazon Mechanical TurkFreshly collected/generated data via a 0.5+ million crowdPaid
Custom human-generated datasetsTelus InternationalFreshly collected/generated data via a 1+ million crowdPaid
Custom machine-generated datasetsSynthesis AISynthetic data generationPaid
Custom machine-generated datasetsHazySynthetic data platformPaid
Custom machine-generated datasetsOpenAI GPT-4LLM for AI training data generationFreemium
Natural Language Processing (NLP)Amazon reviews datasetDataset includes product reviews and Meta DataFree
Natural Language Processing (NLP)The Big Bad NLP Database (BBNLPDB)Over 300 datasets for NLP modelsFree
Natural Language Processing (NLP)Wikipedia Links dataCross-document coreference dataset labeled via links to WikipediaFree
Open datasetsGoogle dataset searchOpen source dataset search engineFree
Open datasetsAWS public datasetsDatasets in areas including biology, meteorology, astronomy, and others.Free
Open datasetsKaggle datasetsData provided by companies and studentsFree
Public government datasetsData.Gov.ukOver 47,000 non-personal UK government dataFree
Public government datasetsEU open data portalOver 1.6 million datasetsFree
Public government datasetsData USAOver 47,000 reportsFree
Image datasetsGoogle’s open imagesOver 9 million imagesFree
Image datasetsCoco datasetOver 200K labelled imagesFree
Image datasetsImagenetOver 14.1 million annotated imagesFree for
non-commercial
use
Image datasetsBaidu ApolloScape DatasetAnnotated images for autonomous driving systemsFree
Image datasetsWaymo Open DatasetImage dataset for autonomous vehicle researchFree for
non-commercial
use
Audio datasetsESC-50Over 2K environmental audio recordingsFree
Audio datasetsSpeech commands datasetOver 65000 crowdsourced speech data unitsFree
Audio datasetsFree music archive (FMA)Over 106K tracksFree
Audio datasetsFlickr audio caption corpus
Over 8K audio recordings of spoken descriptions for images from the Flickr8K dataset
Free
Audio datasetsCommon voiceCrowdsourced database for speech recognition dataFree
Healthcare DatasetsMIMIC Critical Care Database
Health-related data of over 40K patients from the Beth Israel Deaconess Medical Center.
Free
Healthcare DatasetsHealthdata.govOver 2K health-related datasets from the U.S. government.Free
OthersGitHubA comprehensive library of datasetsFree & Paid
Notes from the table:
  • If we missed any dataset, the last row of the table has a comprehensive list of free and paid datasets.
  • This list is made from data gathered from the websites of the datasets.
  • The quantities mentioned in the description column might change with time.
  • ‘Free for non-commercial use’ means the dataset is free for researchers and academicians.

What are ML datasets?

A machine learning dataset is a collection of data that is used to train the model. A dataset acts as an example to teach the machine learning algorithm how to make predictions. The common types of data include:

  • Text data
  • Image data
  • Audio data
  • Video data
  • Numeric data

The data is usually first labeled/annotated in order for the algorithm to understand what the outcome needs to be. Click here to learn more about data annotation.

Why prepare datasets for machine learning?

Preparing and choosing the right dataset is one of the most crucial steps in training an AI/ML model. It can be the determinant between the success and failure of the AI/ML development project. 

There are 3 key purposes of an AI/ML dataset:

  1. To train the model
  2. To measure the accuracy of the model once it is trained
  3. To improve the model once it is deployed in a live setting.

Work with a data partner

You can transfer the preparation process of the dataset to a data collection or generation service provider. You can work with a data crowdsourcing platform or company.

You can also select a data partner based on specific data types:

What are the types of ML datasets?

The whole dataset that is collected is separated into 3 subsets which are as follows:

1. Training dataset

This is one of the most important subsets of the whole dataset, comprising about 60% of the total dataset. This set comprises the data that will initially be used to train the model. In other words, it helps teach the algorithm what to look for in the data. 

For instance, a vehicle license plate recognition system will be trained with image data with labels indicating the location (e.g., front or rear of the car) and the data format of the license plates of vehicles and similar objects to learn what to detect and what to avoid.

Figure 1. Sample dataset for a license plate detection system1

A collage of car images with their number plates labelled with a red box tag. An example of image datasets for ml model training.

2. Validation dataset

This subset is about 20% of the total dataset and is used to evaluate all the parameters of the model after the training phase is complete. The validation data is known data that helps in identifying any shortcomings in the model. This data is also used to identify if the model is over/underfitting

3. Test dataset

This subset is input at the final stage of the training process and accounts for the last 20% of the dataset. The data in this subset is unknown to the model and is used to test the accuracy of the model. In simpler words, this dataset will show how much your model has learned from the previous 2 subsets.

The ratio of training, validation, and testing data in datasets for ml. To explain the portions of datasets for ml. 60 percent is the training set, 20% is the validation set, and 20% is the testing set.

Where can ML datasets be sourced from?

Sourcing a dataset depends on the requirements and scope of the project. This section highlights some popular sources of acquiring datasets to train AI and machine learning models.

1. Custom human-generated datasets

Datasets can also be prepared with fresh data that is collected or generated by humans. Data collection services and companies offer vast pools of workers that help prepare human-generated datasets for machine learning.

Some popular names include:

2. Custom machine-generated datasets

Customer machine-generated datasets made by generative AI tools, particularly for models like Generative Adversarial Networks (GANs), have transformed the landscape of data creation and augmentation. Creating datasets using generative AI addresses several challenges in machine learning. Studies have used LLMs to generate training data for machine learning models.2

In situations where collecting real-world data is expensive, time-consuming, or ethically challenging, generative models can supplement or even replace traditional data collection methods. 

For instance, medical imaging datasets can be augmented using GANs to generate more samples of rare conditions, making it easier to train models that can detect and diagnose these conditions. 

Additionally, in domains like computer vision (CV), generating diverse data helps mitigate model overfitting and improve the robustness of the trained models. This synthesized data, when used carefully alongside real data, can help train more effective and accurate machine learning models, while saving resources and time in the data collection phase.

Some popular names offering synthetic data through generative AI models include:

  • Synthesis AI
  • Hazy
  • LLMs Like GPT-4 by OpenAI

3. Natural language processing datasets

NLP datasets are used for speech recognition, text analytics, and language translation. These types of datasets are large in size and require heavy computational power.

Some popular NLP datasets include: 

  • Amazon Reviews
  • The Big ad NLP Database
  • Wikipedia Links Data

4. Open datasets

These ready-to-use datasets are freely available online for anyone to download, modify, and distribute without legal or financial restrictions. These datasets are regularly updated and are compatible with most ML frameworks. The only drawback is that open datasets lack personalization.

Popular open datasets include:

  • Google dataset search
  • AWS public datasets
  • Kaggle datasets

5. Public government datasets

These datasets are used for government projects that are implemented for the public. For example, these datasets can include a certain population’s census or demographic data. These datasets can be used to make policies or train AI/ML models for immigration decision-making, chatbots that answer citizen queries, city infrastructure planning systems, etc.

Popular public government datasets include:

  • Data.Gov.uk
  • EU open data portal
  • Data USA

6. Image datasets

Image datasets include both image and video data. This type of dataset is used to train computer vision systems for facial recognition, autonomous vehicle systems, retail security systems, etc. These datasets required high-quality image annotation to be used.

Popular image datasets include:

  • Google’s open images
  • Coco dataset
  • Imagenet
  • Baidu ApolloScape Dataset
  • Waymo Open Dataset

7. Audio datasets

These datasets are used to train AI/ML models for voice recognition, music recognition, etc. 

Popular audio datasets include:

  • Environmental audio datasets
  • Speech commands dataset
  • Free music archive (FMA)
  • Flickr audio caption corpus
  • Common voice

8. Healthcare Datasets

These datasets are used to train medical imaging systems or medical diagnosis systems. They are usually large in size and require heavy computational and high-quality medical annotation.

Popular healthcare datasets include:

  • MIMIC Critical Care Database
  • Healthdata.gov

You can also check out our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

Further reading

If you have any questions or need help finding a vendor, feel free to contact us:

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External resources

Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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