Uses/Applications of Machine Learning

This article will conceptualize the definition of machine learning and the uses/applications of Machine learning in the real world.
Source: Markus spiske unsplash

MACHINE LEARNING – It’s a part/subset of Artificial Intelligence.
It is the ability of machines, i.e., computers to learn and improve from past experiences or data, without being explicitly programmed. For example –

In the above diagram, you can see that to reach college, different transportations have been used. If you take the bus, it will take 50 minutes; if you take the metro, it will take 40 minutes; and if you take an auto-rickshaw, it will take 35 minutes to reach. So when you are getting late, using this past information, you will take the transport taking less time to reach college i.e. Auto-rickshaw.

Similarly, the human brain analyses all past experiences and improves the future.

Machine Pattern Algorithm – 

Past Experience 🡪 Analyze 🡪 Prediction

Machine Learning has many algorithms like Decision Tree, Regression model, etc, which constantly analyze and learn from the data to improve their future prediction.

Process of Machine Learning Modeling –

Past Data 🡪 M.L. model 🡪 Intelligence 🡪 New Data 🡪 Predictions

Uses of Machine Learning

There is n number of applications offered by Machine learning from simpler to advanced. 

  1. Social Media utilities

Why do we use social media?

We use social media because social media platforms give better news feed and ads according to the interest of a specific user. 

According to the users and their interests/benefits, these platforms use Machine Learning algorithms. A lot of machine learning happens when you use social media, like-

  • People, you may know the list: Facebook uses all the data from your profile like – your friend list, people visiting your profile often, likes and views on your pictures, etc, and forms a list of people you can add to your friend list. This is Machine learning as it’s nothing but collecting data and improving predictions.
So this is how Facebook decides what goes into the "people you may know"  section | HelloGiggles
  • YouTube: Songs and video suggestions are also Machine Learning.
How YouTube's Suggested Videos Work - YouTube
  1. Image Recognition
Facebook Just Pushed Its Facial Recognition Into a Creepy New Future

It’s a very common and important application of Machine Learning. In this application, the platform recognizes the face and sends the information related to that to the user. Example – Let’s look into what Facebook does; let’s say you upload a picture with a group of friends on your Facebook profile. Machine Learning helps Facebook to study the face structure, unique features, and other projections and match them with the people in your friend list. In this manner, Facebook instantly recognizes people with you in the picture.

  1. Product Recommendations
Intelligent Product Recommendation Template for Online Stores

Have you ever done online shopping from sites like Amazon, Flipkart, etc? If yes, you might have noticed after buying a product you keep getting emails or product recommendations from that app/website according to your taste. These apps use Machine Learning to collect information about a user’s choice by looking at the products they buy, the products they search for, and the products left in the cart. This isn’t magic; these recommendations are made only after analyzing all of your experience when you visited the app/website.

  1.   Search Engine Filtering

Machine Learning is used by Google, Firefox, and other engines to refine search results for the user. There are machine learning algorithms in the backend of a search engine created for searching the particular user query. The page being opened mostly by a user remains at the top for many days. Similarly, the pages user did not open are considered to be of no help and hence marked unimportant by the algorithm, which improves and filters the search results every time.

How Search Engines Work [Beginner's Guide to SEO] - Moz
  1. Prediction
Building user experiences for Ola's PWA while keeping it ultra light | by  Tanya Rao | ux.tanya | Medium

Have you ever booked a cab? If yes, when you book a cab using apps like Ola, the app estimates the rough cost for that ride. Machine Learning plays a big role in estimating the price by looking at the demand of the customer. 

Machine learning is also used to predict traffic in specific areas. When we use Google maps, we see areas having heavy traffic and less traffic, it uses Machine Learning to collect information like GPS navigations to build a map of traffic at a specific time.

How to Predict Traffic on Google Maps for Android | TechWiser
  1. Supporting Customers using online mediums
How to Sound Human in the Age of Chatbots

Today, every website whether study-related or shopping-related offers 24*7 customer support. Either they assign executives chatting or calling the users or they make chatbots which can help users till the executives aren’t necessarily needed. The Chatbot collects information about the customers and answers them, they advance with time. It is believed that Chatbots give better answers because they use M.L. Algorithms.

  1. Email spamming and Malware Refining
Applied Text-Classification on Email Spam Filtering [Part 1] | by Sarah  Mestiri | Towards Data Science

There are various spam filters used by clients that use emails regularly. Spam filters use machine learning techniques like – Rule-based, multi-layer, and tree induction and they update regularly. Similarly, malware is also detected using some security programs powered by machine learning as they understand a specific pattern of coding.


Machine Learning is present in each core of life. It makes a certain work much more approachable and easier. 

Machine Learning: the main goal, some examples and facts - online  presentation

Many companies nowadays are using machine learning in their applications to make processing faster and save time and effort.


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