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When it comes to machine learning, data is key. Without data, there can be no training of models and no insights gained. Thankfully, there are many sources from which you can obtain free machine learning datasets. To dig deeper into the intricacies of preparing data for machine learning, including the process of AI training, can provide valuable insights. Find the most useful open source datasets, and learn what to look out for before acquiring one.
When it comes to machine learning data (ML data), there are many different sources that you can use for machine learning datasets. The most common sources include:
Academic and research institutions
Technology companies
Government and public sector
Cloud service providers
Open-source communities
Corporate and industry specific platforms
data generated by Artificial Intelligence (AI)
Individual enthusiasts collecting and sharing data online
One important thing to note is that the format of the data will affect how easy or difficult it is to use the dataset. Different file formats can be used to collect data, but not all formats are suitable for machine learning models. Example: Text files are easy to read, but they have no information about the variables being collected. On the other hand, CSV files (comma-separated values) have both the text and numeric information in one place, making them convenient for machine learning models. For further insights into how data preprocessing plays a critical role in making datasets more compatible with machine learning models, visit our detailed guide on data preprocessing.
Example: Text files are easy to read, but they have no information about the variables being collected. On the other hand, CSV files (comma-separated values) have both the text and numeric information in one place, making them convenient for machine learning models.
It’s also important ensure that the formatting consistency of your dataset is maintained when it is manually updated by different people. This prevents inconsistencies when using a dataset that has been updated over time. For your machine learning model to be accurate, you need high-quality, consistent input data!
Find the top 20 free machine learning datasets below. And learn more about how to choose the right dataset for your purpose.
Top 20 Free Machine Learning Datasets
Top 20 Free ML Data Sets
The more data you have to train with, the better, but data alone isn’t enough. It’s just as important to make sure that the datasets are relevant to the task at hand and of high quality. For those delving into the complex world of machine learning in finance, ensuring data relevance and quality becomes paramount. Exploring the applications of machine learning in finance can provide invaluable insights into how to select and utilize datasets effectively for financial models.
To save you the trouble of sifting through all the options, we have compiled a list of the top 20 free datasets for machine learning.
Open Datasets
Datasets on open dataset platforms are ready to use with many popular machine learning frameworks. The datasets are well organized and regularly updated, making them a valuable resource for anyone looking for quality data.
#1 Kaggle Datasets
If you’re looking for high-quality datasets to train your models with, there’s no better place to start than Kaggle. With over 1TB of data available and constantly updated by an engaged community that contributes new code or input files to help shape the platform, it’s hard not to find what you need!
The UCI Machine Learning Repository is a well-known dataset source that contains a variety of datasets popular in the machine learning community. The datasets produced by this project are of high quality and can be used for various tasks. The user-contributed nature means that not every dataset is 100% clean, but most have been carefully curated to meet specific needs without major issues.
If you’re looking for large datasets that are ready to use with AWS services, look no further than the AWS Public Datasets repository. Here, datasets are organized around specific use cases and come preloaded with tools that integrate with the AWS platform. A key benefit that sets the AWS Open Data Registry apart is its user feedback feature, which allows users to add and modify datasets.
Google’s Dataset Search is a relatively new tool that makes it easy to find datasets, regardless of their source. Datasets are indexed based on a variety of metadata, making it easy to find what you need. While the selection isn’t as robust as some of the other options on this list, it’s growing every day.
Find open source datasets for your machine learning project.
Public Government Datasets / Government Data Portals
The power of big data analytics is also being realized on government level. With access to demographic data, governments can make decisions that better meet the needs of their citizens, and predictions based on these models can help policymakers design better policies before problems arise.
#5 Data.gov
Data.gov is the US government’s open data site which provides access to various industries, such as healthcare and education, through various filters including budgeting information as well as performance scores of schools across America.
The site offers access to over 250,000 different datasets compiled by the US government. The site includes data from federal, state, and local governments, as well as non-governmental organizations. The datasets cover a wide range of topics including climate, education, energy, finance, health, safety, and more.
The European Union’s Open Data Portal is a one-stop-shop for all your data needs. It offers datasets published by many different institutions across Europe – from 36 different countries. With an easy-to-use interface that allows you to search by specific categories, this site has everything a researcher could hope for when searching for publicly available information.
The financial sector has embraced machine learning with open arms, and it’s no surprise why. Compared to other industries where data is harder to come by, finance and economics offer a treasure trove of information that’s perfect for AI models looking to predict future outcomes based on past performance.
Datasets in this category can help you predict things like stock prices, economic indicators, and exchange rates.
#7 Nasdaq Data
Nasdaq Data provides access to financial, economic and alternative data sets. The data is available in two different formats:
Time series (date/time stamp) and
Tables – numeric/sorted types, including strings for those who need them
You can download either a JSON or CSV file, depending on your preference. This is a great resource for financial and economic data, including everything from stock prices to commodities.
The World Bank is an invaluable resource for anyone looking to understand global trends, and this database contains everything from population demographics to key indicators relevant to development work. It’s open without registration, so you can access it at your convenience.
The World Bank’s open data is the perfect source for large-scale analysis. The information it contains includes population demographics, macroeconomic data, and key development indicators to help you understand how countries around the world are doing on different fronts!
A picture is worth a thousand words, and this is especially true in the field of computer vision. With the growing popularity of autonomous vehicles, facial recognition software is increasingly being used for security purposes. The medical imaging industry also relies on databases of photos and videos to correctly diagnose patient conditions.
Image Datasets can be used for Facial Recognition
#9 ImageNet
The ImageNet dataset contains millions of color images that are perfect for training image classification models. While this dataset is more commonly used for academic research, it can also be used to train machine learning models for commercial purposes.
The CIFAR datasets are small machine learning image datasets commonly used in computer vision research. The CIFAR-10 dataset contains 10 classes of images, while the CIFAR-100 dataset contains 100 classes of images. These datasets are perfect for training and testing image classification models.
The Coco dataset is a large-scale dataset for object detection, segmentation, and captioning. This dataset is perfect for training and testing machine learning models for object detection and segmentation.
The current state of the art in machine learning has been applied to a wide variety of fields, including speech and language recognition, language translation, and text analytics. Natural language processing datasets are typically large and require a lot of computing power to train machine learning models.
#12 The Big NLP Database
The 841 datasets are an excellent resource for NLP-related tasks, including document classification and automatic image captioning. The collection contains many different types of data that you can use to train your machine translation or language modeling algorithms.
Yelp is a great way to find businesses in your area. The app lets you read reviews from other people who’ve already tried it, so you don’t have to do any research. With 8.6 million reviews and hundreds of thousands of curated images, Yelp’s review dataset is a gold mine for any business looking to conduct market research.
This dataset contains all reviews for products on Amazon. It contains more than 2 billion records, including product descriptions and prices! This research was conducted to analyze how people engage with these online communities before making a purchase or sharing their opinion about a particular product.
Over 2000 articles are collected in two pre-processed machine learning datasets from the BBC for natural language processing. However, it is available for non-commercial and research purposes only.
High Quality Customized Datasets for Machine Learning by clickworker
At clickworker, we understand the importance of high-quality data. Our international crowd of 6 million Clickworkers builds customized machine learning datasets. We offer a wide variety of datasets in different formats, including
If you’re looking to analyze audio data, these datasets are perfect for you.
Audio Datasets can be used for Speech Recognition
#16 Common Voice
This open source dataset of voices for training speech-enabled technologies was created by volunteers who recorded sample sentences and reviewed other users’ recordings.
The Free Music Archive (FMA) is an open dataset for music analysis that includes full-length and HQ audio, pre-computed features such as spectrogram visualization, or hidden text mining with machine learning algorithms. It includes track metadata such as artist names and albums – all organized into genres at various levels within this hierarchy.
The data requirements for autonomous vehicles are immense. In order to interpret their surroundings and react accordingly, these cars need high-quality datasets, which can be hard to come by. Fortunately, there are a number of organizations that collect information about traffic patterns, driving behavior, and other data sets that are important to autonomous vehicles.
#18 Waymo Open Dataset
This project provides a set of tools to help collect and share data for autonomous vehicles. The dataset includes information about traffic signs, lane markings, and objects in the environment. Lidar and high-resolution cameras were used to capture 1000 driving scenarios in urban environments across the US. The collection includes 12 million 3D labels and 1.2 million 2D labels for vehicles, pedestrians, cyclists, and signs.
This dataset consists of over 100 hours of driving data collected by Comma AI in San Francisco and the Bay Area. The data was collected using a comma.ai device, which uses a single camera and GPS to provide live feedback on driving behavior. The data includes information about traffic, road conditions, and driver behavior.
The BaiduApolloScape dataset is a large-scale autonomous driving dataset containing more than 100 hours of driving data collected in various weather conditions. The data includes information on traffic, road conditions, and driver behavior.
These are just 20 of the best free machine learning datasets available today. With so many to choose from, you’re sure to find one that’s perfect for your needs. So get started on your next project and take advantage of all the free data that’s out there!
Datasets will only benefit your machine learning model if the data is specific and relevant to the topic addressed. Generic open source datasets may not contain the information you need in order to train your model. Therefore, one option to consider is building your own machine learning dataset.
What you can expect:
An important benefit of custom machine learning datasets is that you can segment your data into specific groups, allowing you to tailor your algorithms. When creating a custom dataset, it’s important to ensure that your algorithm doesn’t overfit the data, meaning that it can adapt and make predictions for new data.
Machine learning is a powerful tool that can be used to improve the performance of business processes. However, it can be difficult to get started without the right data. That’s where customized machine learning data sets enter the picture. These data sets are tailored to your needs, so you can start using machine learning right away.
The data is customizable and available on demand. You no longer have to settle for pre-packaged data sets that don’t fit your exact needs. It’s now possible to request additional data or customized columns. You can also specify the format of the data so it’s easy to work with in your preferred software platform.
Things to Consider Before You Acquire a Dataset
When it comes to machine learning, data is key. The more data you have, the better your models will perform. However, not all data is created equal. Before you acquire a dataset for your machine learning project, there are a few things you need to consider:
Plan your project carefully before acquiring a dataset.
Purpose of the data: Not all datasets are created equal. Some datasets are designed for research purposes, while others are meant for production applications. Make sure the dataset you acquire is appropriate for your needs.
Data type and quality: Not all data is of equal quality. Make sure the dataset contains high-quality information that is relevant to your project.
Relevance to your project: Datasets can be extremely large and complex, so make sure the data is relevant to your specific project. For example, if you’re working on a facial recognition system, don’t buy a dataset of images that only include cars and animals.
When it comes to machine learning, the phrase “one size does not fit all” is especially true. That is why we offer customized datasets that are tailored to your specific business needs.
What Makes a Good Dataset for Your Machine Learning Project?
A good machine learning dataset has a few key characteristics: it’s large enough to be representative, of high quality, and relevant to the task at hand.
Features of a good dataset for machine learning
Quantity is important because you need enough data to train your algorithm properly. Quality is important to avoid problems with bias and blind spots in the data. If you don’t have enough high-quality data, you run the risk of overfitting your model – that is, training it so well on the available data that it performs poorly when applied to new examples. In such cases, it’s always a good idea to seek advice from a data scientist. Relevance and coverage are key factors to consider when collecting data. Use live data whenever possible to avoid problems with bias and blind spots in the data.
To summarize: A good machine learning dataset contains variables and features that are appropriately structured, has minimal noise (no irrelevant information), is scalable to large numbers of data points, and can be easy to work with.
How Do You Divide ML Datasets?
A dataset is split into three parts: training, validation, and testing data.
A machine learning dataset is divided into training, validation, and test sets. Machine learning typically uses these datasets to teach algorithms how to recognize patterns in the data.
The training dataset in machine learning is the data that helps teach the algorithm what to look for and how to recognize it when it sees it in other data sets.
A validation set is a collection of known-good data against which the algorithm can be tested.
The test set is the final collection of unknown-good data against which you can measure the performance and make adjustments.
Quick Tips for your Machine Learning Project
Make sure all data for machine learning is labeled correctly. This includes both input and output variables for your model.
Avoid using unrepresentative samples when training your models.
Use a variety of datasets to effectively train your models.
Choose datasets that are relevant to your problem domain.
Choose machine learning algorithms carefully; not all algorithms are suitable for every type of dataset.
Data Annotation
You have the data, but it is not quite ready yet for the machine learning algorithm? We assist you with preprocessing – labeling, annotating and categorizing – the data. Contact our Managed Service or learn more about our annotation service.
Machine learning is becoming more and more important in our society – and it is not just for big companies, anyone can train machine learning models and apply them to their use case. To get started, you need to find a good dataset and database. Once you have those, your data scientists and data engineers can take your tasks to the next level. If you’re stuck in the data collection stage, it may be worth reconsidering how you approach collecting your data.
FAQs on Machine Learning Datasets
What are Machine Learning Datasets?
Machine learning datasets are the training material for machine learning algorithms. A dataset is an example of how machine learning helps make predictions, with labels that represent the outcome of a given prediction (success or failure). The best way to get started with machine learning is by using libraries like Scikit-learn or Tensorflow which allow you to perform most tasks without writing code.
A training dataset in machine learning is simply a set of information that can be used to make predictions about future events or outcomes based on historical data. Datasets are typically labeled before they are used by machine learning algorithms so that the algorithm knows what outcome it should predict or classify as an anomaly. For example, if you were trying to predict whether or not a customer would churn, you might label your dataset “churned” and “not churned” so the machine learning algorithm can learn from past data. Machine learning datasets can be created from any data source – even if that data is unstructured. For example, you could take all the tweets that mention your company and use that as a machine learning dataset.
To learn more about machine learning and its origins, read our blog post on the History of Machine Learning.
What Machine Learning Methods are there?
There are three main types of machine learning methods: supervised (learning from examples), unsupervised (learning through clustering) and reinforcement learning (rewards). Supervised learning is the practice of teaching a computer how to recognize patterns in data. Techniques that use supervised learning algorithms include: random forest, nearest neighbors, weak law of large numbers, ray tracing algorithm and SVM algorithm.
Why Do You Need Datasets for Your AI Model?
Machine learning datasets are important for two reasons: they allow you to train your machine learning models, and they provide a benchmark for measuring the accuracy of your models. Datasets come in a variety of shapes and sizes, so it’s important to choose one that is appropriate for the task at hand.
Machine learning models are only as good as the data they’re trained on. The more data you have, the better your model will be. That’s why it’s important to have a large volume of processed datasets when working on AI projects – so that you can train your model effectively and get the best results.
What Type of Data is Used for Machine Learning Datasets?
In the context of data for machine learning, and general data handling, data can be categorized into several main types based on its nature and characteristics:
Numerical Data:
Discrete Data: Consists of integer values that count something, such as the number of occurrences. Examples include the number of students in a class or the number of cars in a parking lot.
Continuous Data: Can take on any value within a range. Examples include height, weight, or temperature, where measurements could theoretically be infinitely precise.
Categorical Data:
Nominal Data: Data that can be divided into categories but not ordered. Examples include colors, names, labels, and geographic locations (e.g., countries, cities).
Ordinal Data: Categorical data with a specific order. Examples include rankings, scales (such as agree to disagree), or levels of education (e.g., elementary, high school, college).
Binary Data: A special type of categorical data with only two categories (0 and 1). This includes yes/no, true/false, on/off, etc.
Textual Data: Consists of words, sentences, or paragraphs. It requires special processing techniques such as natural language processing (NLP) to generate useful features for machine learning models.
Time-Series Data: Data points indexed, listed, or graphed in time order. This is often used for forecasting and analyzing data points spaced over time intervals.
Spatial Data: Information about physical locations. It might include latitude and longitude coordinates, zip codes, or data from geographic information systems.
Multimedia Data: Includes images, audio, and video data. Each of these types of multimedia can be used in machine learning models, but typically requires substantial preprocessing to extract features.
Structured Data: Highly organized and formatted in a way that is easily searchable in relational databases. Examples include Excel files or SQL databases.
Unstructured Data: Not organized in a predefined way. This includes text, video, photos, and web pages, which are more complex to process and analyze.
Semi-structured Data: A form of organized data that does not conform to the formal structure of data models associated with relational databases but contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields. Examples include JSON and XML.
What Use Cases are there for Machine Learning Datasets?
There are many different types of machine learning datasets. Some of the most common are text data, audio data, video data and image data. Each type of data has its own unique set of use cases.
Text data is a great choice for applications that need to understand natural language. Examples include chatbots and sentiment analysis.
Audio datasets are used for a variety of purposes, including bioacoustics and sound modeling. They can also be useful in computer vision, speech recognition or music information retrieval.
Video datasets are used to create advanced digital video production software, such as motion tracking, facial recognition and 3D rendering. They can also be created for real-time data collection.
Image datasets for machine learning are used for a variety of different purposes, including image compression and recognition, speech synthesis, natural language processing and more.
Where Can I Find Machine Learning Datasets?
There are many sources for machine learning datasets. Some popular sources include the UCI Machine Learning Repository, Kaggle Datasets, and Amazon's AWS Datasets.
What is the Difference between Dataset and Database?
A dataset is a file that contains data, while a database is an organized collection of datasets. A database can be divided into multiple tables, each of which consists of rows and columns. A dataset can be stored in a database, but it can also exist independently.
How Do You Prepare a Machine Learning Dataset?
The first step is to understand your data. This includes understanding the features (columns) and the target variable (what you're trying to predict). Once you have a good understanding of your data, you can start to clean it. This includes dealing with missing values, outliers, and other issues. Once your data is clean, you can split it into training and test sets. The training set is used to train your machine learning model, while the test set is used to evaluate the performance of your model.
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