ML algorithms are in fact designed to improvise with time and for this, they need quality data from time to time.

Data is core to any ML or AI project and it is estimated that roughly the project needs ten times the examples your project has degrees of freedom. Having heaps of machine learning datasets is very crucial that sometimes even after you think you have enough data you might end up concluding the existing data is not enough. Having data at that scale though might result in overfitting, at times, it is absolutely necessary for the algorithms to learn about all details and noise. Machine learning algorithms are in fact designed to improvise with time and for this, they need quality data from time to time. However, machine learning experts find it difficult to source data continuously to keep the algorithm working. Analytics Insight lists out the top 10 sources for finding machine learning datasets in 2022.

1. Kaggle: A very versatile platform to source data for your machine learning project. Each data source is a community in itself where you can discuss the project apart from sourcing data. You can find a vast number of real-life datasets in different formats and sizes. Using the 'Kernels' associated with each database, you can analyse the database even before putting it to use. For prediction problems, notebooks with algorithms associated with specific datasets come as a great help.

The link for the Kaggle dataset is https://www.kaggle.com/datasets

2. Amazon datasets: Of course, it should be the default dataset repository for the data it gathers by virtue of the significance it holds in fulfilling the everyday needs of the people. They are well into providing open datasets to the projects which need enormous and diverse data in commercial realms. It comes with a search box and user feedback feature, where users can modify the data. The advantage of this repository lies in the description and usage examples it provides for each dataset.

Users can find the AWS dataset at  https://registry.opendata.aws/

3. UCI Machine Learning repository: It is a resourceful machine learning repository created by the University of California. The data from this source is being used by the student and teacher community for a long. The dataset is very much conducive for data analysis because it makes the job of a data scientist pretty easy by storing data in categories based on the type of machine learning problem. Users can find categorised data for like univariate, multivariate time-series problems, regression, classification, or recommendation systems, some of which are cleaned and are ready to use.

This database contains databases, domain theories, and data generators that are hugely helpful in the analysis of ML algorithms.

Users can find the link here:  https://archive.ics.uci.edu/ml/index.php

4. Google's Datasets Search Engine: It is akin to a web browser for datasets. According to Google's website, the search engine provides a collaborative ecosystem apart from allowing users to choose from millions of datasets. With different filters, now it is even easier to find the specific data targeted towards the specific need of the ML problem at hand. Get data in the format you like such as text, tables and images that fit into the project you are working on. According to Google's website, more and more structured data will be made available in the coming years.

Google's datasets can be accessed at  https://toolbox.google.com/datasetsearch

5. Microsoft's datasets: Microsoft's repository holds a collection of free data sets in different domains like natural language processing, computer vision, and domain-specific sciences. The "Microsoft Research Open Data" exists over the cloud thereby making the data access and collaboration of data science experts from different geographical areas an easy affair. It also offers a few curated data sets that were used in published research articles. As most datasets are provided as plain text files, they are suitable for importing into Python, R, and other analysis tools. Apart from downloading the data, users can deploy these datasets for analysis into Microsoft Azure, Microsoft's cloud platform.

Download the datasets from Microsoft here:  https://msropendata.com/

6. Government datasets: Governments publish their data as part of their transparency policy. These datasets are extremely useful particularly when the projects you are working on needs data at the testing and validation stage. Some of the datasets made available by the governments of different countries are as follows:

• European data portal- A data repository set up by European Union for access to European Government datasets

Link to the website: data.europa.eu

• US Gov Data: This is US Government's official website where you will find data and tools for data analysis.

Link to the website: Data.gov

• OpenDataNI: It is the UK government's repository created to keep the datasets available for social research and policymaking.

Link to the website: https://www.opendatani.gov.uk/

• Indian Government Dataset: Set up by NIC it aims to provide access to data in formats which is available in both open and machine-readable format.

Link to the website: https://data.gov.in/

7. Awesome public dataset collection: It provides high-quality datasets categorized into topics such as economics, biology, agriculture, education, etc. Most of the data is free but it is suggested to check for the licensing before downloading the dataset.

Find the link to the above datasets here: https://github.com/awesomedata/awesome-public-datasets  

8. Computer vision datasets: At Visual Data, users can get access to data pertaining to deep learning techniques used for image processing. Experiments in image processing and video processing need specific data related to images to build computer vision (CV) models. Users can access a dataset by a particular CV subject such as Semantic Segmentation, Image captioning, Image generation, etc.

Find the link to computer vision datasets here: https://www.visualdata.io/

9. Lionbridge AI: It is a multilingual crowdsourcing service that includes document, text, and product classification. They provide datasets classified in various formats such as text, image, audio, and video files. Users can use their text categorization services to train models for categorizing product listings or blocking privy public information. It offers crowdsourced data entry in around 300 hundred languages and a team of more than 5,00,000 contributors from different parts of the world, doing data entry and data cleansing.

Access Lionbridge's data services here: https://www.lionbridge.com/technology/

10. Scikit-learn dataset: Scikit-learn is unique in the sense that it provides dummy as well as real data. The datasets can be accessed through sklearn. datasets package or using general dataset API. The dummy datasets can be downloaded using python commands such as, load_boston([return_X_y]), load_iis([return_X-y]), etc without having to import information from external sources. However, these data sets are not suitable for real-world projects.

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