How to Get Datasets for Machine Learning

Over the last few decades, the improvements in information technologies have brought us exponential growth in numerous industries.

As our products, services, and inventions keep improving, a unique observation has been made: even as the internet offers its users unimaginable amounts of information, fresh data is the most valuable resource in the digital business environment.

Tech companies go to great lengths and even undermine the privacy of their clients and social media users to accumulate as many sources of information as possible, bringing an end to internet privacy.

However, no matter how upsetting the death of anonymity may be, it leads to fascinating technological leaps.

Machine learning and its contribution to Artificial Intelligence (AI) creates revolutionary software that learns from presented data to better predict outcomes and expected behavior in the future.

With enough information, we push technology to new heights and teach it to make correct decisions.

Machine learning is much slower than manual, human information absorption, but the goal is not imitation but strengthening the systems where our natural limitations fail.

With enough data, we can create software that offers incredible precision, which can be used for noble goals, such as the fast and accurate discovery of cancerous cells and faster implementation of treatment.

Of course, there are also concerning ways to use machine learning, such as facial recognition, which may be used to track and profile individuals in the future.

Machine learning requires tons of information from various data sets. In this article, we will describe a few ways an up-and-coming data scientist like yourself can get datasets for machine learning.

Some methods may involve web scraping public data on the internet so you can gather knowledge by yourself.

In such cases, you might need a datacenter proxy to cover your IP and protect data aggregation tasks.

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However, a data center network offers great proxy server addresses, but because they come in bulk, you may encounter big websites that have already banned them.

Instead of a datacenter proxy, you can use residential proxies, but more on that later. Let’s talk about the gathering of data sets for machine learning.

Free Data Set Sources

On the internet, you can find plenty of websites that offer free data sets.

While not every set will possess valuable and applicable information, using them is a great way to polish your data science and machine learning skills.

To start strong, let’s talk about Google Dataset Finder.

If anything, Google is amazing at finding and collecting information on the web to make it accessible via a query on a comfortable and easily understandable search engine.

Once again, the tech giant did not disappoint. Dataset Finder lets you request available data sets based on presented keywords.

With over 30 million available sets, you will be able to access the necessary information for machine learning.

VisualData is another crucial source of data sets that provides visual information for machine learning.

You can practice training software to recognize pictures and polish your data science skills with many visual data sets.

Going through these sets gives us the crucial precision training that we already discussed before.

With enough samples, you can develop software that finds differences between objects that could be impossible to notice with a human eye.

Different data sets offer great opportunities for the implementation of machine learning in every niche: from facial recognition to fields like medicine, agriculture, and many more.

Web Scraping Public Data

You can build your data set by extracting public information from the internet with web scrapers.

After downloading the HTML code of targeted websites and pages, data has to go through the parsing process to transform into a data set.

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Web scrapers can aggregate information from some websites without interruptions, but if you decide to target retailers with online shops and sensitive prices, get yourself a proxy server to avoid an IP ban.

If you are employed by a company that requires you to web scrape competitors to create a data set, you might need secure residential proxies supplied by business-focused proxy providers.

Once you get access to multiple proxy IPs, you can operate multiple web scrapers at the same time, as well as cycle addresses on bots to avoid suspicion.

The internet is an ever-expanding world of information, where anyone can find valuable data that suits their needs.

For machine learning, there are millions of already created data sets where scientists and tech enthusiasts already collected the knowledge to assist others.

However, if the available sets do not offer the information you desperately need, you can always extract the data yourself.

If everything fails, you can purchase valuable data sets or discover the ones supplied by resellers.

With millions of available sources, you should have no trouble finding data sets, especially if your goals are educational.

Even if you stumble upon a unique niche with no available data, you can learn a new skill and build a new data set yourself.

Who knows, maybe such an experience will motivate users to create unique data sets and put them for sale!

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Jonathon Spire

Jonathon Spire

Tech Blogger at Jonathon Spire

My diverse background started with my computer science degree, and later progressed to building laptops and accessories. And now, for the last 7 years, I have been a social media marketing specialist and business growth consultant.

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Jonathon Spire

I blog about a range of tech topics.

For the last 7 years I have been a social media marketing specialist and business growth consultant, so I write about those the most.

Full transparency: I do review a lot of services and I try to do it as objectively as possible; I give honest feedback and only promote services I believe truly work (for which I may or may not receive a commission) – if you are a service owner and you think I have made a mistake then please let me know in the comments section.

– Jon