15 Everyday Applications of Machine Learning

Last Updated on June 29, 2021 by Henry John

Machine learning is powering a lot of contemporary technologies.

According to ExpertSystem.com, “Machine learning is an application of artificial intelligence that provides system the ability to automatically learn and improve from experience without being explicitly programmed to do so”.

Machine learning is a big deal. Consider the fact that $28.5 billion was allocated to machine learning worldwide during the first quarter of 2019.

Since 2000, the rate of increase in machine learning startups is 14x.

But how is machine learning being applied in the world today?

In this article, we are going to explore some of the application of machine learning.

Let’s roll the ball.

1. Self-driving vehicles

Although, self-driving vehicles are still largely experimental, they are getting closer to flood the auto market. At the core of self-driving development is machine learning.

There are numerous machine learning algorithms that are used by self-driving vehicle to make autonomous decisions.

Some of these algorithms are Scale-Invariant Feature Transform (SIFT) that enables self-driving cars to detect partially visible objects, TextonBoost that enables them to more accurately recognize objects and Histogram of Oriented Gradients (HOG) that enables self-driving cars to see how and in what direction the intensity of images changes.

The machine learning algorithms in essence enables self-driving cars to detect an object even if it’s partially visible, recognize an object (ball, human being, other vehicles), and predict movement of objects.

Machine learning (which includes deep learning) are what enables self-driving cars to know when to move, turn, or stop among other things.

2. Search Engines

Almost everybody uses search engines one way or another. Advanced search engines like Google search, YouTube search, and Amazon search applies machine learning in a number of ways.

The obvious use of machine learning in search engines are in the building of ranking algorithms. More so, search engines like Google also use machine learning for pattern detection (this helps to identify spam or duplicate content), in image search to understand photos, and to understand and searchers intent among others.

So whenever you run a search on advanced search engines, bear in mind that machine learning is at work.

3. Stock Predictions

For years, people have been in the business of stock prediction. Different techniques and tricks have been and are being adopted by expert stock predictors to make stock predictions.

But recent time, one technology is beginning to champion the cause of stock prediction. And that technology is machine learning.

Machine learning algorithms are being deployed to predict stock prices.

4. Medical Diagnosis

There are a number of ways machine learning is transforming the health sector, and one of these ways are in Medical Diagnosis.

Machine learning algorithms are being deployed in different ways to run diagnose patients.

According to reports from the Institute of Medicine at the National Academics of Science, Engineering and Medicine, “diagnostic errors contribute to approximately 10 percent of patients deaths”.

This is a really troubling report.

Another report by Johns Hopkins study claimed that more than 250,000 people in the US die every year from medical errors and that medical errors and medical error are the third leading cause of deaths.

These are issues machine learning is tackling head on to proffer more accurate medical diagnosis, and this is helping to save lives.

I have been using symptom checkers like Ada that enables me run a mini-diagnosis on myself and loved ones from my phone.

The technology is incredibly accurate and helpful.

Machine learning medical diagnosis are mostly applied in chatbots (like Ada), oncology, pathology and rare diseases.

5. Social Media NewsFeed

The internet has 4.54 billion user and there are 3.725 billion active social media users.

Social media is a prominent thing, the numbers speaks for themselves. It allows people to follow other people and keep track of people they follow.

Hence, the news feed is one of the most important place in every social media platform.

Years before machine learning revolutionized social media news feed, social media news feed were unfiltered. There was too much noise.

On Twitter for instance, everything you tweet shows up to every one of your followers and everything your followings (people you follow) tweets shows up to you.

Literally everything!

This results in social media platforms throwing too much to users, too much that it’s easy to lose track of those they love.

It was so bad it almost ruined Twitter.

But today, using machine learning social media platform can study user behavior, in order to determine the most engaging contents that will be put before them (in their newsfeed).

Machine learning in social media news feed that noises are eliminated and users only see contents that they would most likely love and engage deeply with.

6. Fraud Detection

Over 3 million identity theft and fraud reports were received in 2018, 1.4 million were fraud-related and 25 percent of those cases reported money was lost.

In the same year, consumers reported losing about $1.48 billion to fraud related complaints, according to Insurance Information Institute.

Shift Processing reported that “credit card fraud increased by 18.4 percent in 2018 and is still climbing”.

Fraud is a major problem in every part of the world, internet fraud and its application in fraud detection has brought promising results.

Feedzai, a data science company that detects fraud and handles $1 billion payments volume per day, claims that machine learning solution can detect up to 95 percent of all fraud.

7. Online Advertising

Machine learning is being applied in advertising offline and online. Although its more prominent in online advertising.

According to Orchid Richardson, V.P. and M.D. at the IAB’s Data Center of Excellence, “already, 95% of advertisers have terabytes upon petabytes of demographic data, including personal data, location information, and interests they can use to target prospects they know almost nothing about. Artificial intelligence is a way to tame that data and take it to the next level”.

Using machine learning advertisers are taming those data to run better ads.

According to Amazon, “machine learning helps Amazon Web Services customers use historical data to predict future outcomes, which can lead to better business decisions. Machine learning techniques are core to the digital advertising industry”.

8. Medical Imaging

Machine learning is being applied significantly in medical imaging.
Medical imaging refers to several different technologies that are used to view the human body in order to diagnose, monitor, or treat medical conditions.

Medical imaging data such an x-rays, CAT scans, MRIs, and other testing modalities are being harnessed and more accurately analyzed using machine learning.

Machine learning algorithms are able to learn from such imaging data and become a valuable ally to radiologists and pathologists.

Today, machine learning are being applied to identify cardiovascular abnormalities, detect fractures and other musculoskeletal injuries, and diagnosis of neurological diseases, flagging thoracic complications and screen common cancers among numerous other things.

9. Spam Detection

According to SpamLaws.com, “spam accounts for 14.5 billion messages globally per day. In other words, spam makes up 45% of all emails”.

To put things in perspective, for a most every two email messages on is spam.

Since am using Gmail, it’s hard to relate with this stats, if one doesn’t know what’s happening behind Gmail.

I can’t recall the last time I saw a spam message and this is because Gmail is applying machine learning to detect and block spam messages.

In 2015, Google claimed that Gmail spam filters that (relies on machine learning) is able to filter out 99.9% of Gmail spam.

In 2019, Google revealed that Gmail is now blocking 100 million more spam emails per day, thanks to TensorFlow (a machine learning platform).

10. Digital Virtual Assistants

There are an estimated 3.25 billion digital voice assistants being used in devices around the world in 2019, according to Statista.

Digital assistants like Alexa, Siri, Bixby and Google Assistants are gaining more ground with every passing seconds.

And at the heart of these digital assistants is machine learning.

Machine learning is being applied in digital assistants to perform various tasks for its users, like answering users’ queries, providing weather updates, managing calendar events, controlling home functions, providing news, providing traffic update, managing emails, and tracking to-do list and personal health among others.

Using machine learning, these digital assistants learns about and understand users’ preferences and perform most of its tasks according to users’ preference.

11. Language Translation

Google Translate and iTranslate are popular language translators that can translate between English and over 100 other languages.

Such applications has transformed the way we communicate with people who don’t understand our language.

As you may have already surmised, machine learning is being applied at the core of the development of such advanced language translators.

Computer system powered by these application are now able to learn human language and even translate from one language to another.

12. Personalize Learning

Personalize learning is an educational approach that aims to customize learning for each student’s strengths, needs, skills, and interests. Personalized learning is a concept that has been around for ages.

And for the first time it’s now stands a chance of becoming highly scalable. The current dominate system of learning that most of us has been exposed to, is the one teacher, many students system.

John Pane, an education researcher at the RAND Corporation expressed that “we couldn’t afford to have an individual teacher for every student, so we developed a way of teaching large group”.

However, today, machine learning is changing things up and personalized learning powered by machine learning is gaining more grounds.

Using machine learning, EdTech platforms are now able to deploy an individual teacher for every students (in a way).

Machine learning is being used by EdTech platform offering personalize learning to boost students’ engagement and results, allocate resource to tasks of value and automate content scheduling and delivery.

13. Gmail Smart Responses

In 2017, Gmail launched the Smart Reply feature that saves users time by suggesting quick responses to messages.

When I upgraded my Gmail app, I was amazed by how intelligent the feature was. It suggests responses that I would normally give.

And sometimes when I’m lacking words to reply an email, the Smart Reply featured almost always save my day.

The Smart Reply suggests three responses based on the email you received. Upon your selection, the response can then be sent.

By applying machine learning, the Smart Reply feature is able to read your emails, understand it and know the kind of responses that would be suitable for such emails.

14. Dynamic Pricing

Dynamic pricing is the practice of varying the price for a product or service to reflect changing market conditions, in particular the changing of a higher price at a time greater demand” according to Lexico.

During festive periods, like Christmas or New Years, there tend to be an increase in price of certain goods and services, as a result of increased demands.

For instance, flight prices tends to be really high at those periods. Machine learning is being applied to implement the dynamic pricing strategy, for different purposes.

Hopper, a travel app, uses machine learning to predict ticket prices with 95% accuracy up to one year in advance.

Machine learning enables business implement dynamic pricing on a large scale while taking into account hundreds of pricing factors.

15. Weather and Traffic Predictions

Machine learning is being applied to make more accurate and precise weather and traffic predictions.

Apps like Google Map uses machine learning to help users avoid traffic by showing when there is likely tube traffic at a specific destination.
Uber, Lyft, and Bolt also applies machine learning to predict traffic.

Weather, AccuWeather and Weather Underground are all apps that uses machine learning to provide more accurate weather predictions.

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