Everyday Machine Learning Applications You Might Not Know About!

Last updated by Rishabh Dev Choudhary on Sep 02, 2024 at 12:05 PM | Reading time: 11 minutes

Machine learning sounds like an idea from a science fiction movie. However, it’s not only real, but also the driving force behind some of the most recent technologies we use today. Why does this matter? Well, if you had this impression that machine learning is really hard and that it belongs only to people with a PhD in computer science or for masterminds that build artificial intelligence (AI) in some secret lab – you are wrong. In fact, you have been using machine learning applications every day and you didn’t even know about it!

Therefore, in this blog, we will take you through some of the most interesting machine learning applications that you might be new to. Whether it is aiding companies in making better judgments or further improving our conversation with gadgets, artificial intelligence is all over and that is something that has to be recognized by us today!

What Is Machine Learning?

Before we dive into the details, let us first give a brief description of what machine learning actually is. You may think of machine learning as a manner in which computers are able to learn from data without being given instructions on each step. In contrast to a human providing a device with an algorithm that tells it what to do next, machine learning algorithms give the device the capability to process data for itself, identify patterns, and conclude decisions upon what they find.

In short, you’re teaching a computer to fish, not giving it one. The wild part is that the computer keeps on getting better at fishing as time goes on, learning something from every catch.

There are two major types of machine learning:

Supervised Learning: This is supervised learning whereby the machine is taught using a labeled dataset. It is akin to tutoring a child by providing him examples and then testing him if he has learnt it in the end.
Unsupervised Learning: In unsupervised learning, the machine is given a whole bunch of data and told to do everything on its own. The machine has to try and find patterns or relationships in the data it was given to play with. It’s like being given a puzzle to solve but not being shown what the finished product is supposed to look like.

In recognition of these basic concepts, let us now explore some practical machine learning applications to demonstrate how machine learning is silently yet significantly propelling our lives towards a better, simpler and more captivating future.

Also read: What is Machine Learning? A Comprehensive Guide

Top 10 Machine Learning Applications You Might Not Know About

Not only tech giants or the most hardcore data scientists, but everyone is applying Machine Learning in their everyday lives and here are ten real-life machine learning applications you’ve probably already encountered in your life:

1. Machine Learning Application - Email Spam Filtering

How it works: Remember when your inbox used to be flooded with spam? That stopped being a problem for most of us after machine learning algorithms were used to analyze massive troves of emails. They simply figured out what was spam and what wasn’t, and then filtered the incoming messages for all users.

Impact: By using the spam filter, Gmail lets you avoid 99.9% of spam messages even before they reach your email inbox!

2. Machine Learning Application - Virtual Personal Assistants

How it works: Siri, Alexa and Google Assistant are not just talking to you; they are learning from you. These virtual assistants powered by artificial intelligence learn your language and can predict your intentions; therefore, over time, they will provide better answers. A machine learning algorithm is used in building virtual assistants to decode the information encoded in your speech and to make predictions on what you might be seeking, thus improving their performance going forward.

Impact: 3.25 billion voice-activated assistants were being used globally as of 2021.

3. Machine Learning Application - Online Fraud Detection

How it works: Every time you swipe your credit card, machine learning algorithms go through the details of the transaction to check for any kind of fraud. These algorithms will spot those patterns of fraudulent activities and alert them.

Impact: The global fraud detection and prevention market is projected to grow from $52.82 billion in 2024 to $255.39 billion by 2032, at a CAGR of 21.8%, according to Fortune Business Insights.

4. Machine Learning Application - Social Media Algorithms

Machine Learning Application - Social Media Algorithms

How it works: Ever wondered why you keep seeing that cat video on Facebook or Instagram? The kind of content that you are most likely to engage with is served to you by the social media platform, and machine learning does this by learning from your behavior.

Impact: Because Facebook is built on posts, it uses machine learning to power the News Feed and help predict what posts will keep you scrolling.

Also read: Machine Learning vs. Data Science — Which Has a Better Future?

5. Machine Learning Application - Online Recommendations

How it works: Netflix actually knows what you want to watch before you do. It's all thanks to the power of machine learning. Essentially, recommendation systems take into account your viewing history, cross reference that with other users and BOOM, they suggest some shows/movies you’re probably going to love.

Impact: Netflix’s recommendation system impacts 80% of the shows watched on the service.

6. Machine Learning Application - Smart Home Devices

How it works: Smart thermostats like Nest learn your schedule and temperature preferences, and then they keep building that pattern until they can consistently predict when you’ll be home and how warm or cool you like the house.

Impact: Smart home devices make up 28% of houses in the US.

7. Machine Learning Application - Healthcare Diagnostics

How it works: Machine learning programs are transforming health care by scanning medical records for disease diagnoses. The learning algorithms can notice things that doctors tend to miss—signs of disease that, when they appear in combination, might indicate an illness.

Impact: Several factors will drive the cost of AI in healthcare globally to $45.2 billion by 2026.

8. Machine Learning Application - Financial Market Predictions

How it works: Machine learning algorithms review historical market data and changes in stock prices to simulate models of trading behavior and make predictions about future prices.

Impact: The machine learning hedge funds have been able to surpass the conventional funds by even 2% per annum.

9. Machine Learning Application - Language Translation

How it works: Google Translate and other translation apps get better over time through the process of machine learning. These use ever larger bodies of text in different languages to train a computer to translate phrases in a more context-aware way.

Impact: With the ability to translate 108 languages and support over 500 million users daily, Google Translate is quite an impressive tool.

10. Machine Learning Application - Supply Chain Optimization

How it works: Machine learning predicts demands, manages inventory and optimizes logistics to help companies reduce costs, minimize waste and ensure on-time product delivery.

Impact: The market for Global Supply Chain Management is estimated to reach $42.46 billion by 2027 growing at a CAGR of 10.4%.

5 Unsupervised Machine Learning Applications

Now that we’ve gone through some overall examples, let’s dive deeper into unsupervised machine learning – this is when the machine needs to learn things on its own without any labeled data. Here are five cool unsupervised machine learning applications:

1. Machine Learning Application - Customer Segmentation

How it works: Businesses employ unsupervised learning to segment the customers into different groups on the basis of their behavior and purchase patterns to do targeted marketing campaigns and better customer service.

Example: E-commerce sites categorize their users according to their purchasing behavior in order to suggest personalized offers.

2. Machine Learning Application - Anomaly Detection

How it works: Unsupervised learning can be very beneficial for the purpose of pinpointing outliers or anomalies in data. This has wide applications starting from fraudulent pattern identification in financial transactions to flaws detection in manufacturing processes.

Example: Identifying abnormal trends in network data that could lead to a cyber intrusion.

3. Machine Learning Application - Market Basket Analysis

How it works: Retailers leverage unsupervised learning to study data of shopping carts and discover what products are often purchased together so as to better layout stores and put products.

Example: Grocery stores like putting chips and salsa next to each other on the same aisle because customers who buy one usually buy the other.

4. Machine Learning Application - Dimensionality Reduction

How it works: Unsupervised learning simplifies a complex data structure by reducing the number of variables that are superimposed on the data; as a result, it becomes easier for us to perceive a simple (abstracted) structure in the reduced space of superimposed variables.

Example: Utilizing Principal Component Analysis (PCA) to decrease data complexity to aid in analysis. The human text does not simply give an answer to the question but provides a solution using the given approach.

5. Machine Learning Application - Image Compression

How it works: Unsupervised learning algorithms can compress images by finding the patterns and redundancies in the data. The file size without significantly reducing the quality of the image.

Example: Compressing big image files on your smartphone.

5 Supervised Machine Learning Applications

Supervised learning uses labeled data to predict or decide. Here are five cool supervised machine learning applications:

1. Machine Learning Application - Sentiment Analysis

How it works: Supervised learning algorithms are able to understand the sentiment of a text by being fed with labeled datasets (positive, negative, neutral). This is widely used in studying social media posts, reviews and customer feedback.

Example: Companies that monitor Twitter mentions to measure what the public is saying about their brand.

2. Machine Learning Application - Handwriting Recognition

How it works: Handwritten text can be recognized and interpreted using supervised learning, which requires training on labeled instances of handwriting.

Example: Converting handwritten notes to digital text on a tablet or smartphone.

3. Machine Learning Application - Face Recognition

How it works: Supervised learning empowers facial recognition systems by training on a dataset of labeled faces. Everything from unlocking smartphones to identifying people in photos is its utility.

Example: Apple’s Face ID technology.

4. Machine Learning Application - Predictive Maintenance

How it works: Supervised learning can predict when machinery is likely to fail by analyzing historical maintenance data. This allows companies to perform maintenance before a breakdown occurs.

Example: Aircraft engines being serviced on the basis of predicted wear and tear.

5. Machine Learning Application - Medical Imaging Diagnostics

How it works: Supervised learning is revolutionizing medical diagnostics by studying images (e.g. X-rays or MRIs) and detecting abnormalities with high performance.

Example: Detecting tumors in radiology images with greater precision.

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FAQs: Machine Learning Applications

Q1. What is the difference between supervised and unsupervised machine learning?

Supervised learning uses labeled data to make predictions, while unsupervised learning finds patterns and relationships in unlabeled data.

Q2. How is machine learning used in healthcare?

Machine learning is used for diagnostics, predicting patient outcomes, and even in drug discovery by analyzing medical data.

Q3. Can machine learning predict stock market trends?

Yes, machine learning is used in financial markets to analyze historical data and make predictions about future stock prices, though it’s not foolproof.

Q4. What are some common machine learning applications in daily life?

Common machine learning applications include email spam filtering, personalized online recommendations, facial recognition, and virtual personal assistants like Siri.

Q5. Is machine learning the same as AI?

Machine learning is a subset of artificial intelligence (AI). While AI encompasses a broad range of technologies, machine learning specifically focuses on systems that learn and improve from experience.

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Rishabh Dev Choudhary

Machine Learning

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Everyday Machine Learning Applications You Might Not Know About!

Contents

Rishabh Dev Choudhary

Attend our Free Webinar on How to Nail Your Next Technical Interview

How to Nail your next Technical Interview

Worried About Failing Tech Interviews?

Tools to Enhance Full Stack Development with AI

The Future of Data Science: Emerging Trends and Opportunities

Data-Driven Decision Making: Your Roadmap to Business Success

The Business Impact of Machine Learning: Real-world Case Studies

What is OpenAI? Everything You Need to Know

Mock Interviews for Generative AI: Essential Practice to Land Top AI Roles

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

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