Top 10 Machine Learning Applications with Real Life Examples

Top 10 Machine Learning Applications With Real-Life Examples

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OK, Google… What are the applications of machine learning?

Knowingly or unknowingly, we are utilizing machine learning applications in real life. 

By the end of this blog, you might be amazed to know that machine learning is embedded in your routine activities. Everything includes machine learning, whether you are going to the office using Google Maps or receiving recommendations of movies on your phone. 

Curious, right? 

Without further ado, let us dive into the Top 10 Popular Machine Learning Applications with real life examples. 

Key Takeaways

  • Meaning and importance of Machine Learning 
  • Real-life applications of Machine Learning algorithms 
  • Day-to-day examples of Machine Learning tools 

What is Machine Learning?

Machine Learning is a sub-part of Artificial Intelligence. 

Within AI, Intelligent machines analyze the existing statistical data and through predictive analysis, produce a set of algorithms that simulate human thinking capabilities and behaviour.

The importance of ML tools can be predicted by the following quote by Dave Waters –  

“In a few years, artificial intelligence virtual assistants will be as common as the smartphone.”

Now that you are aware of the meaning and importance of Machine Learning let us explore its applications in daily life. 

Also read: Difference Between Artificial Intelligence, Machine Learning And Deep Learning

Machine Learning Applications in Real Life

Machine learning has varied applications in our day-to-day life.

Here’s a list of the top 10 Machine Learning Applications:

  1. Image Recognition
  2. Traffic Prediction
  3. Commuting
  4. Recommendations
  5. Self Driving Cars
  6. Virtual Personal Assistant 
  7. Language Translation 
  8. Fraud Detection
  9. Medical Diagnosis
  10. Sentiment Analysis 

Let us know in detail how machine learning is used in day-to-day life.

1. Image Recognition

Image recognition is the simplest ML application. The ML algorithm digitally detects faces, places, and objects and provides suggestions from their stored databases. 

General real-life examples of image recognition applications include Social Media Platforms like Facebook, Photo Galleries on your smartphones, etc. 

Whenever you upload a picture with your friends on Facebook, it automatically suggests tagging the person with the name. Not only faces, but the Facebook image detecting algorithm also suggests location. 

The intelligent photo clarification features are there in many phones. It enables the segregation of images based on location, people, group photos, landscape, cate, documents, and every other type of feature. 

2. Traffic Prediction

For traffic prediction, the ML tools gather the information and data from the users and store them in the database. 

For example, whenever you travel by navigating Google Maps then, your data, like time taken, traffic on the route, etc., is stored in its database. 

Using the statistics of the past few days, prediction is done on traffic and approximate time for vehicles to reach the destination.

3. Commuting

If you travel by cab, you already utilise a machine learning application. 

The apps like Uber, Ola, and Rapido detect your live location and travel history. Based on your travel history, it provides options for pick-up and drops locations. 

The machine learning algorithm analyses every trip data to offer you a personalised commuting service.

4. Recommendations

Numerous companies use machine learning algorithms to provide recommendations to their customers. 

For instance, when you watch something on Netflix, your data like when did you play, when did you stop, what did you watch, everything is stored in its database. According to your view history, you get personalised recommendations. 

When you buy any product or even surf through certain apps on apps like Amazon, Flipkart, Myntra, etc., those products and their related products are recommended to you on your social media feed and even Google.

5. Self-Driving Cars

You must have heard of Tesla, right?

Then you will surely agree if I say that Self-Driving Car is an innovative ML application.

Tesla uses an unsupervised learning method to detect humans and objects while driving.

6. Virtual Personal Assistant

Virtual personal assistants like Alexa, Siri, and Google Assistant detect the user’s voices and perform the tasks using ML algorithms.

We often ask Alexa or Siri to play music, open an email, call someone, or do other such tasks. Our voice is stored in the cloud and analysed by algorithms.

7. Language Translation

Machine learning facilitates the quick translation of words from one language to another. 

For example, Google Translate uses the sequence-to-sequence algorithm for translating different languages.

8. Fraud Detection

These days, when everyone relies on UPI’s, credit cards, wallets, etc. for payments, leading to online fraud happening at an alarming rate. 

The machine learning algorithm detects fraudulent practices like fake accounts, online money stealing, etc., in online transactions and prevents us from doing that. 

9. Medical Services

With the use of ML algorithms, medical services have been enhanced to a great extent. 

Machine learning benefits various medical services like disease diagnosis, detecting brain tumours, predicting patient wait time, maintaining patient records, planning therapy, etc. 

Medical technology is developing and growing rapidly due to machine learning. 

10. Sentiment Analysis

Sentiment Analysis is a real-time application of machine learning. It detects the feeling and emotions of the writer or speaker. 

For example, in any document or email, a sentiment analyzer can determine the tone of the content. It can be even used to review a website based on opinions. 

Conclusion

With that, this blog comes to an end. I think by now, you must have a better understanding of the numerous applications of machine learning and how beneficial it is to people. 

Currently, machine learning engineers are in high demand as there is a vast growth in machine learning. If you have the appropriate skills, you will definitely have a secure future. 

Willing to build your career in this exciting field? Consider pursuing Verzeo’s IBM-certified Machine Learning Course

Frequently Asked Questions (FAQs)

1. What are the main applications of machine learning?

Below listed are the top 10 applications of machine learning:
1. Image Recognition
2. Traffic Prediction
3. Commuting
4. Recommendations
5. Self-Driving Cars
6. Virtual Personal Assistant 
7. Language Translation 
8. Fraud Detection
9. Medical Diagnosis
10. Sentiment Analysis 

2. What are the types of machine learning?

There are 3 types of machine learning:
1. Supervised learning 
2. Unsupervised learning 
3. Reinforcement learning 

3. Is coding required in machine learning?

Yes, some coding experience is required if you want to work in the fields of artificial intelligence and machine learning.

4. What is the salary of a machine learning engineer?

The average salary of a machine learning engineer in India is 7.5 lakhs per year.

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