20+ Datasets for ML (Machine learning) in 2024

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

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:

• 10+ Image Data Collection Services
• 10+ Speech Data Collection Services
• 7+ Video Data Collection Services & Selection Criteria

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

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.

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:

• Clickworker
• Appen
• Amazon Mechanical Turk
• Telus International

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

• Crowdsourced AI Data Collection Benefits & Best Practices
• Top 4 Data Collection Challenges and Their Solutions
• Sentiment Analysis: How it Works & Best Practices

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

External resources

• 1. Han J, Yao J, Zhao J, Liu Y. (2019). Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks. Researchgate. Accessed: 03/Oct/2023.
• 2. Peng, Baolin & Li, Chunyuan & He, Pengcheng & Galley, Michel & Gao, Jianfeng. (2023). Instruction Tuning with GPT-4.  Accessed: 04/December/2023.