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8 resources to get free training data for ml systems

The current technological landscape has exhibited the need for feeding Machine Learning systems with useful training data sets. Training data helps a program understand how to apply technology such as neural networks. This is to help it to learn and produce sophisticated results.

The accuracy and relevance of these sets pertaining to the ML system they are being fed into are of paramount importance, for that dictates the success of the final model. For example, if a customer service chatbot is to be created which responds courteously to user complaints and queries, its competency will be highly determined by the relevancy of the training data sets given to it.

To facilitate the quest for reliable training data sets, here is a list of resources which are available free of cost.

Kaggle

Owned by Google LLC, Kaggle is a community of data science enthusiasts who can access and contribute to its repository of code and data sets. Its members are allowed to vote and run kernel/scripts on the available datasets. The interface allows users to raise doubts and answer queries from fellow community members. Also, collaborators can be invited for direct feedback.

The training data sets uploaded on Kaggle can be sorted using filters such as usability, new and most voted among others. Users can access more than 20,000 unique data sets on the platform.

Kaggle is also popularly known among the AI and ML communities for its machine learning competitions, Kaggle kernels, public datasets platform, Kaggle learn and jobs board.

Examples of training datasets found here include Satellite Photograph Order and Manufacturing Process Failures.

Registry of Open Data on AWS

As its website displays, Amazon Web Services allows its users to share any volume of data with as many people they’d like to. A subsidiary of Amazon, it allows users to analyze and build services on top of data which has been shared on it.  The training data can be accessed by visiting the Registry for Open Data on AWS.

Each training dataset search result is accompanied by a list of examples wherein the data could be used, thus deepening the user’s understanding of the set’s capabilities.

The platform emphasizes the fact that sharing data in the cloud platform allows the community to spend more time analyzing data rather than searching for it.

Examples of training datasets found here include Landsat Images and Common Crawl Corpus.

UCI Machine Learning Repository

Run by the School of Information & Computer Science, UC Irvine, this repository contains a vast collection of ML system needs such as databases, domain theories, and data generators. Based on the type of machine learning problem, the datasets have been classified. The repository has also been observed to have some ready to use data sets which have already been cleaned.

While searching for suitable training data sets, the user can browse through titles such as default task, attribute type, and area among others. These titles allow the user to explore a variety of options regarding the type of training data sets which would suit their ML models best.

The UCI Machine Learning Repository allows users to go through the catalog in the repository along with datasets outside it.

Examples of training data sets found here include Email Spam and Wine Classification.

Microsoft Research Open Data

The purpose of this platform is to promote the collaboration of data scientists all over the world. A collaboration between multiple teams at Microsoft, it provides an opportunity for exchanging training data sets and a culture of collaboration and research.

The interface allows users to select datasets under categories such as Computer Science, Biology, Social Science, Information Science, etc. The available file types are also mentioned along with details of their licensing.

Datasets spanning from Microsoft Research to advance state of the art research under domain-specific sciences can be accessed in this platform.

GitHub.com/awesomedata/awesomepublicdatasets

GitHub is a community of software developers who apart from many things can access free datasets. Companies like Buzzfeed are also known to have uploaded data sets on federal surveillance planes, zika virus, etc. Being an open-source platform, it allows users to contribute and learn about training data sets and the ones most suitable for their AI/ML models.

Socrata Open Data

This portal contains a vast variety of data sets which can be viewed on its platform and downloaded. Users will have to sort through data which is currently valid and clean to find the most useful ones. The platform allows the data to be viewed in a tabular form. This added with its built-in visualization tools makes the training data in the platform easy to retrieve and study.

Examples of sets found in this platform include White House Staff Salaries and Workplace Fatalities by US State.

R/datasets

This subreddit is dedicated to sharing training datasets which could be of interest to multiple community members. Since these are uploaded by everyday users, the quality and consistency of the training sets could vary, but the useful ones can be easily filtered out.

Examples of training datasets found in this subreddit include New York City Property Tax Data and Jeopardy Questions.

Academic Torrents

This is basically a data aggregator in which training data from scientific papers can be accessed. The training data sets found here are in many cases massive and they can be accessed directly on the site. If the user has a BitTorrent client, they can download any available training data set immediately.

Examples of available training data sets include Enron Emails and Student Learning Factors.

Conclusion

In an age where data is arguably the world’s most valuable resource, the number of platforms which provide this is also vast. Each platform caters to its own niche within the field while also displaying commonly sought after datasets.  While the quality of training data sets could vary across the board, with the appropriate filters, users can access and download the data sets which suit their machine learning models best. If you need a custom dataset, do check us out here, share your requirements with us, and we’ll more than happy to help you out!

The need for quality training data | Blog | Bridged.o

What is training data? Where to find it? And how much do you need?

Artificial Intelligence is created primarily from exposure and experience. In order to teach a computer system a certain thought-action process for executing a task, it is fed a large amount of relevant data which, simply put, is a collection of correct examples of the desired process and result. This data is called Training Data, and the entire exercise is part of Machine Learning.

Artificial Intelligence tasks are more than just computing and storage or doing them faster and more efficiently. We said thought-action process because that is precisely what the computer is trying to learn: given basic parameters and objectives, it can understand rules, establish relationships, detect patterns, evaluate consequences, and identify the best course of action. But the success of the AI model depends on the quality, accuracy, and quantity of the training data that it feeds on.

The training data itself needs to be tailored for the end-result desired. This is where Bridged excels in delivering the best training data. Not only do we provide highly accurate datasets, but we also curate it as per the requirements of the project.

Below are a few examples of training data labeling that we provide to train different types of machine learning models:

2D/3D Bounding Boxes

2D/3D bounding boxed | Blog | Bridged.co

Drawing rectangles or cuboids around objects in an image and labeling them to different classes.

Point Annotation

Point annotation | Blog | Bridged.co

Marking points of interest in an object to define its identifiable features.

Line Annotation

Line annotation | Blog | Bridged.co

Drawing lines over objects and assigning a class to them.

Polygonal Annotation

Polygonal annotation | Blog | Bridged.co

Drawing polygonal boundaries around objects and class-labeling them accordingly.

Semantic Segmentation

Semantic segmentation | Blog | Bridged.co

Labeling images at a pixel level for a greater understanding and classification of objects.

Video Annotation

Video annotation | Blog | Bridged.co

Object tracking through multiple frames to estimate both spatial and temporal quantities.

Chatbot Training

Chatbot training | Blog | Bridged.co

Building conversation sets, labeling different parts of speech, tone and syntax analysis.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.

Data Management

Cleaning, structuring, and enriching data for increased efficiency in processing.

Image Tagging

Image tagging | Blog | Bridged.co

Identify scenes and emotions. Understand apparel and colours.

Content Moderation

Content moderation | Blog | Bridged.co

Label text, images, and videos to evaluate permissible and inappropriate material.

E-commerce Recommendations

Optimise product recommendations for up-sell and cross-sell.

Optical Character Recognition

Learn to convert text from images into machine-readable data.


How much training data does an AI model need?

The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on. Although, as an unspoken rule, machine learning enthusiasts understand that larger the dataset, more fine-tuned the AI model will turn out to be.

Validation and Testing

After the model is fit using training data, it goes through evaluation steps to achieve the required accuracy.

Validation & testing of models | Blog | Bridged.co

Validation Dataset

This is the sample of data that is used to provide an unbiased evaluation of the model fit on the training dataset while tuning model hyper-parameters. The evaluation becomes more biased when the validation dataset is incorporated into the model configuration.

Test Dataset

In order to test the performance of models, they need to be challenged frequently. The test dataset provides an unbiased evaluation of the final model. The data in the test dataset is never used during training.

Importance of choosing the right training datasets

Considering the success or failure of the AI algorithm depends so much on the training data it learns from, building a quality dataset is of paramount importance. While there are public platforms for different sorts of training data, it is not prudent to use them for more than just generic purposes. With curated and carefully constructed training data, the likes of which are provided by Bridged, machine learning models can quickly and accurately scale toward their desired goals.

Reach out to us at www.bridgedai.com to build quality data catering to your unique requirements.