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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!

10 free image training data resources online

Not too long ago, we would have chuckled at the idea of a vehicle driving itself while the driver catches those extra few minutes of precious sleep. But this is 2019, where self-driving cars aren’t just in the prototyping stage but being actively rolled out to the public. And, remember those days when we were marveled by a device recognizing it’s users face? Well, that’s a norm in today’s world. With rapid developments, AI & ML technologies are increasingly penetrating our lives. However, developments of such systems are no easy task. It requires hours of coding and thousands, if not millions, of data to train & test these systems. While there are a plethora of training data service providers that can help you with your requirements, it’s not always feasible. So, how can you get free image datasets?

There are various areas online where you can discover Image Datasets. A lot of research bunches likewise share the labeled image datasets they have gathered with the remainder of the network to further machine learning examine in a specific course.

In this post, you’ll find top 9 free image training data repositories and links to portals you’re ready to visit and locate the ideal image dataset that is pertinent to your projects. Enjoy!

Labelme

Free image training dataset at labelme | Bridged.co

This site contains a huge dataset of annotated images.

Downloading them isn’t simple, however. There are two different ways you can download the dataset:

1. Downloading all the images via the LabelMe Matlab toolbox. The toolbox will enable you to tweak the part of the database that you need to download.

2. Utilizing the images online using the LabelMe Matlab toolbox. This choice is less favored as it will be slower, yet it will enable you to investigate the dataset before downloading it. When you have introduced the database, you can utilize the LabelMe Matlab toolbox to peruse the annotation records and query the images to extricate explicit items.

ImageNet

Free image training dataset at ImageNet | Bridged.co

The image dataset for new algorithms is composed by the WordNet hierarchy, in which every hub of the hierarchy is portrayed by hundreds and thousands of images.

Downloading datasets isn’t simple, however. You’ll need to enroll on the website, hover over the ‘Download’ menu dropdown, and select ‘Original Images.’ Given you’re utilizing the datasets for educational/personal use, you can submit a request for access to download the original/raw images.

MS COCO

Free image training dataset at mscoco | Bridged.co

Common objects in context (COCO) is a huge scale object detection, division, and subtitling dataset.

The dataset — as the name recommends — contains a wide assortment of regular articles we come across in our everyday lives, making it perfect for preparing different Machine Learning models.

COIL100

Free image training dataset at coil100 | Bridged.co

The Columbia University Image Library dataset highlights 100 distinct objects — going from toys, individual consideration things, tablets — imaged at each point in a 360° turn.

The site doesn’t expect you to enroll or leave any subtleties to download the dataset, making it a simple procedure.

Google’s Open Images

Free image training data at Google | Bridged.co

This dataset contains an accumulation of ~9 million images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the biggest dataset to exist with object location annotations.

Fortunately, you won’t have to enroll on the website or leave any personal subtleties to get the dataset allowing you to download the dataset from the site without any obstructions.

On the off chance that you haven’t heard till now, Google recently released a new dataset search tool that could prove to be useful if you have explicit prerequisites.

Labelled Faces in the Wild

Free image training dataset at Labeled Faces in The Wild | BridgedCo

This portal contains 13,000 labeled images of human faces that you can readily use in any of your Machine Learning projects, including facial recognition.

You won’t have to stress over enrolling or leaving your subtleties to get to the dataset either, making it too simple to download the records you need, and begin training your ML models!

Stanford Dogs Dataset

Image training data at Stanford Dogs Dataset | Bridged.co

It contains 20,580 images and 120 distinctive dog breed categories.

Made utilizing images from ImageNet, this dataset from Stanford contains images of 120 breeds of dogs from around the globe. This dataset has been fabricated utilizing images and annotation from ImageNet for the undertaking of fine-grained picture order.

To download the dataset, you can visit their website. You won’t have to enroll or leave any subtleties to download anything, basically click and go!

Indoor Scene Recognition

Free image training data at indoor scene recognition | Bridged.co

As the name recommends, this dataset containing 15620 images involving different indoor scenes which fall under 67 indoor classes to help train your models.

The particular classifications these images fall under incorporated stores, homes, open spaces, spots of relaxation, and working spots — which means you’ll have a differing blend of images used in your projects!

Visit the page to download this dataset from the site.

LSUN

This dataset is useful for scene understanding with auxiliary assignment ventures (room design estimation, saliency forecast, and so forth.).

The immense dataset, containing pictures from different rooms (as portrayed above), can be downloaded by visiting the site and running the content gave, found here.

You can discover more data about the dataset by looking down to the ‘scene characterization’ header and clicking ‘README’ to get to the documentation and demo code.

Well, here are the top 10 repositories to help you get image training data to help in the development of your AI & ML models. However, given the public nature of these datasets, they may not always help your systems generate the correct output.

Since every system requires it’s own set of data that are close to ground realities to formulate the most optimal results, it is always better to build training datasets that cater to your exact requirements and can help your AI/ML systems to function as expected.

7 Best Practices For Creating Training Data

The success of any AI or ML model is determined by the quality of the data used. A sophisticated model using a bad dataset would eventually fail to function the way it was expected to. With such models continually learning from the data provided, it’s necessary to build datasets that can help these model achieve their objectives. 

If you’re still unsure what training datasets are and why are they important to the success of your system. Here’s a quick read to get you up to speed with training data and building high-quality training sets.

While building a dataset sounds like a mundane and tedious task, it determines the success or failure of the model being built. To help you look past the dreadful hours spent on collecting, tagging, and labeling data, here are 7 things to follow when making training datasets. 

Avoid Target Leakage

When building training data for AI/ML models, it’s necessary to avoid any target leakage or data leakage. The issue of data leakage arises when the model is trained on parameters that might not be available during real-time prediction. Since the system already knows all possible outcomes, the output would be unrealistically accurate during training. 

Since data leakage causes the model to overrepresent its generalization error, making it useless for real-world applications. It’s necessary to remove any data from the training set that might not be known during real-time prediction to avoid target leakage issue. Furthermore, to mitigate the risks of data leakage, its necessary to involve business analysts and professionals with the domain expertise to be involved in all aspects of data science projects from problem specifications to data collection to deployment.  

Avoid Training-Serving Skew In Training Sets

Training-serving skew problem arises when the performance during training is different from the performance during serving. The most common reasons for this issue to arise are the discrepancy in how data is handled in training compared to serving, change in data between training and serving. And, the feedback loop between the model and algorithm. 

Exposing a model to training-serving skew can negatively impact the model’s performance, and the model might not function the way it’s expected to. One way to ensure you avoid training-serving skew is by measuring the skew. You can do this by, measuring the difference the performance on training data and the holdout data, the difference between holdout data and ‘next-day’ data, and the difference in performance between ‘next-day’ data and live data.

Make Information Explicit Where Needed 

As mentioned earlier, when working on data science projects, it’s important to involve business analysts and professionals of the domain to be part of the projects. Machine learning algorithms use a set of input data to create an output. This input data is called features, structured in the form of columns. 

Domain professionals can help in feature engineering, i.e., understanding those features that can make the model work. This helps in two primary ways, preparing proper input datasets compatible with the algorithm used and improving the accuracy of the model over time.

Avoid Biased Data When Building Training Sets

When building a training dataset for your AI/ML model, it’s important to make sure the training data is a representation of the entire universe of data. And, not biased towards a set of inputs. 

For example, an e-commerce website that ships products globally wants to use a chatbot to help its users shop better and faster. In such a scenario, if the training data is built only using exchanges/queries from customers of only one region. The system might throw exceptions when a customer from any other region interacts with the bot, given the nuances of language. So, to make sure the system is free of bias, the training data should contain exchanges of all kind of users the e-commerce shop caters to. 

Ensure Data Quality Is Maintained In Training Data 

As stated earlier, the quality of your training data is an essential factor in determining the accuracy and success of AI/ML models. A training dataset that’s filled with bias, and features not available in real-world scenarios would result in the model showing outputs that are far from ground-truths. 

We at Bridged.co have employed two ways of ensuring every dataset we deliver is of the highest quality – consensus approach, and sample review. These approaches make sure that the models trained using these datasets produce results as close to ground-realities as possible. 

Use Enough Training Data

It just isn’t enough to have good-quality data. The dataset you use to train your model must cover all possible variations of the features chosen to train the system. Failing to do so can cause the system function abnormally and produce inaccurate results.

The more features you use to train your model more the data that will be needed to sufficiently train the system. While there is no ‘one size fits all’ when deciding the size of training data. A good rule of thumb for classification models is to have at least 10 times the number of data as you have features, and for regression models, 50 times the number of data as you have features.

Set Up An In-house Workforce or Get A Fully-managed Training Data Solution Provider

Building a dataset is no overnight task. It’s a long tedious process that stretches on for weeks if not months. 

It would be ideal to have an ops team in-house whom you can train, monitor, and ensure the highest quality is maintained. However, it isn’t a scalable solution. 

You can also check out training data solution providers, such as ourselves, to help you with all your training data requirements. A fully-managed solution provider doesn’t just provide you with quality control but also ensure your requirements can be met even if at scale. 


It’s a no brainer that a good quality training dataset is fundamental to the success of your AI/ML systems. These important tips are bound to make sure the training data you build is of the highest quality and helps your system produce accurate results. 

Understanding training data and how to build high-quality training data for ai/ml models

We are living in one of the most exciting times, where faster processing power and new technological advancements in AI and ML are transcending the ways of the past. From conversational bots helping customers make purchases online to self-driving cars adding a new dimension of comfort and safety for commuters. While these technologies continue to grow and transform lives, what makes them so powerful is data.

Tons and tons of data.

Machine Learning systems, as the name suggests, are systems that are constantly learning from the data being consumed to produce accurate results.

If the right data is used, the system designed can find relations between entities, detect patterns, and make decisions. However, not all data or datasets used to build such models are treated equally.

Data for AI & ML models can be essentially classified into 5 categories: training dataset, testing dataset, validation dataset, holdout dataset, and cross-validation dataset. For the purpose of this article, we’ll only be looking at training dataset and cover the following topics.

What Is Training Data

Training data also called training dataset or training set or learning set, is foundational to the way AI & ML technologies work. Training data can be defined as the initial set of data used to help AI & ML models understand how to apply technologies such as neural networks to learn and produce accurate results.

Training sets are materials through which an AI or ML models learn how to process information and produce the desired output. Machine learning uses neural network algorithms that mimic the abilities of the human brain to take in diverse inputs and weigh them, to produce neural activations, in individual neurons. These provide a highly detailed model of how human thought process works.

Given the diverse types of systems available, training datasets are structured in a different way for different models. For conversational bots, the training set contains the raw text that gets classified and manipulated.

On the other hand, for convolution models using image processing and computer vision, the training set consists of a large volume of images. Given the complexity and sophistication of these models, it uses iterative training on each image to eventually understand the patterns, shapes, and subjects in a given image.

In a nutshell, training sets are labeled and organized data needed to train AI and ML models.

Why Are Training Datasets Important

When building training sets for AI & ML models, one needs huge amounts of relevant data to help these models make the most optimal decision. Machine learning allows computer systems to tackle very complex problems and deal with inherent variations of hundreds and thousands or millions of variables.

The success of such models is highly reliant on the quality of the training set used. A training set that accounts for all variations of the variables in the real world would result in developing more accurate models. Just like in the case of a company collecting survey data to know about their consumer, larger the sample size for the survey is, more accurate the conclusion will be.

If the training set isn’t large enough, the resultant system won’t be able to capture all variations of the input variables resulting in inaccurate conclusions.

While AI & ML models need huge amounts of data, they also need the right kind of data, as the system learns from this set of data. Having a sophisticated algorithm for AI & ML models isn’t enough when the data used to train these systems are bad or faulty. Training a system on a poor dataset or a dataset that contains wrong data, the system will end up learning wrong lessons, and generate wrong results. And eventually, not work the way it is expected to. On the contrary, a basic algorithm using a high-quality dataset will be able to produce accurate results and function as expected.

For example, in the case of a speech recognition system. The system can be made on a mathematical model to train the system on textbook English. However, this system is bound to show inaccurate results.

When we talk about language, there is a massive difference between textbook English and how people actually speak. To this add the factors – such as voice, dialects, age, gender – varying among speakers. This system would struggle to handle any cases or conversations that stray from the textbook English used to train it. For inputs having loose English or a different accent or use of slang, the system would fail to function for the purpose it was created.

Also, in a case, such a system is used to comprehend a text chat or email it would throw unexpected results. As a system trained in textbook English would fail to account for abbreviations and emojis used, which are commonly used among people in everyday conversations.

So, to build an accurate AI or ML model, it’s essential to build a comprehensive and high-quality training dataset. To help these systems learn the right lessons and formulate the right responses. While it’s a substantial task to generate such a high volume of data, it is necessary to do so.

How To Build A Training Dataset

Now, that we have understood why training data are integral to the success of an AI or ML model, it’s necessary to know how to build a training dataset.

The process of building a training dataset can be classified into 3 simple steps: data collection, data preprocessing, and data conversion. Let’s take a look at each of these steps and how it helps in building a high-quality training set.

Data Collection

The first step in making a training set is choosing the right number of features for a particular dataset. The data should be consistent and have the least amount of missing values. In case a feature has 25% to 30% of missing values, then this feature should not be considered to be part of the training set.

However, there might be instances when such features might be closely related to another feature. In such a case, it’s advisable to impute and handle the missing values correctly to achieve desired results. At the end of the data collection step, you should clearly know how to handle preprocessing data.

Data Preprocessing

Once the data has been collected, we enter the data preprocessing stage. In this step, we collect the right data from the complete data set and build a training set. The steps to be followed here are:

  • Organize and Format: If the data is scattered across multiple files or sheets, it’s necessary to compile all this data to form a single dataset. This includes finding the relation between these datasets and preprocess to form a dataset of required dimensions.
  • Data Cleaning: Once all the scattered data is compiled to a single dataset, it’s important to handle the missing values. And, remove any unwanted characters from the dataset.
  • Feature extraction: The final step in the data preprocessing step deals with finalizing the right number of features required for the training set. One has to analyze and find out features that are absolutely important for the model to function accurately and select them for faster computations and low memory consumption.

Data Conversion

The data conversion stage consists of the following steps,

  • Scaling: Once the data is placed, it’s necessary to scale the data as per a definite value. For example, a bank application containing transaction amount being important, then it’s required to scale the data on transaction value to build a robust model.
  • Disintegration and composition: There might be certain features in the training data that can be better understood by the model when split. For example, time-series function, where days, month, year, hour, minutes, and seconds can be split for better processing.
  • Composition: While some features can be better utilized when disintegrated, other features can be better understood when combined with another.

This covers the necessary steps to be taken to build a high-quality training set for AI & ML models. While this might help you formulate a framework that helps you build training sets for your system, here’s how you can put these frameworks into action.

Dedicated In-house Team

One of the easiest way for you could be to hire an intern to help you with the task of collecting and preprocessing data. You can also set up a dedicated ops team to help with your training set requirements. While this method provides you with greater control over the quality, it isn’t scalable, and you’ll be forced to look for more efficient methods eventually.

Outsource Training Set Creation

If having an in-house team doesn’t cut it, it would be a smarter move to outsource it, right? Well, not entirely.

Outsourcing your training set creation has its own set of troubles. Right from training people to ensuring quality is maintained to making sure people aren’t cutting slack.

Training Data Solutions Providers

With AI & ML technologies continuing to grow and more companies joining the bandwagon to roll out AI-enabled tools. There are a plethora of companies that can help you with your AI/ML training dataset requirement. We at Bridged.co have served prominent enterprises delivering over 50 million datasets.

And that is everything you need to know about training data, and how to go about creating one that helps you build powerful, robust, and accurate systems.