Machine Learning Examples in Daily Life

Can anyone think of any Enterprise or business operating nowadays without any application of Machine Learning? The answer is “definitely NO”. Now let us understand the concept of Machine Learning with examples.

Machine Learning is the concept to train machine to behave like human mind.

The use of Machine Learning helps organizations to get automatic results in lesser time with the best possible accuracy. In a nutshell, almost all businesses nowadays are driven by machine learning or artificial Intelligence.

ML algorithms help AI to learn and perform desired actions. The ML algorithm learns from the sample input and performs on the basis of the pattern provided in the sample. It never follows the given program instructions. Hence it applies its own knowledge and acts like a human brain.

 A pattern is a particular set of behavioral reactions or a common sequence of actions. Therefore, we can talk about patterns with regard to any area where people use templates.

Now , let us take a few Machine Learning Examples and applications in real daily life.

Social Media

Social Media platform make use of ML and create some excellent features. Facebook, Linkedin ,Pin are common example of this.

Friend Suggestion on Facebook

We spend hours on Facebook surfing and chatting with friends. Facebook always keeps track of us without our notice. The posts the user visits, Likes, Comments, and Shares are in the record of Facebook. What makes it possible? Yes, it Machine Learning Algorithm. The friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone, etc. are memorized by Facebook and further suggestions are given on the basis of this memory.

Face Recognition

When we upload a picture with our friend, Facebook compares the picture with the friend list in the database and recognizes the image. This unique feature is possible due to the Machine Learning algorithm.

Similar Pins

ML also finds its application in Pinterest. The technique of Machine Learning extracts useful information from images and videos. Pinterest uses computer vision to identify the objects ( or pins) in the images and suggests similar pins.

E-mail Spam

Most of the messages we read everyday undergo the process of filteration by email clients. This is very important because a number of viruses also accompany the messages. Previously email clients use to apply rule based Spam filtering technique. This technique was less effective because it can track only cetain spams containing particular words. To overcome this, now Spam filteration with AI spam filteration is in use. This track the spams on the basis of certain algorithms and trends.

email spam

 Virtual Personal Assistants 

Google Assistant, Siri, Alexa are the common and most popular personal assistants.

We Simply ask-Alexa, What is the time now or I am leaving for office, Shall I take umbrella? Voice friendly Alexa with speech recognition system guides us coolly . These smart apps use machine learning technology to understand the natural language questions of user and provide the most relevant answers.

Had anyone thought of such gadgets behaving like humans a few years before? Again it can be said that these are still in their infancy and will change the future in the coming years

Machine Learning or deep learning plays the important role in this. It refines the information of the previous experience and applies it to predict personal preferences.

google assistant

Image Recognition

Image Recognition is one of the most important ML or AI or say Machine Learning examples. Its working principle is the intensity of pixels in black or white or color images. On this basis, it can identify as a digital image. It can also further analyze as pattern recognition, face detection, or face recognition. 

Sentiment Analysis-Machine Learning Examples

This application analyses the mood of the author or presenter. Sentimental Analysing is the real-time determination of the emotions and tone of the author. For example, if a document is written or a video is cast, the sentimental analyzer looks out for the actual mood of the author in the document or video. This is most often seen on Facebook, Youtube, etc.

Smile Emoji HD Stock Images | Shutterstock
Pin on Flaco's StuffEmoji rendering differences enough to identify devices and browsers | The  Daily Swig

Speech Recognition

This application of machine learning can translate speech and lectures into a written text file. The speech is segmented by intensities on time – frequency band as well. Google Assistant and Amazon Alexa uses this technique for speech recognition and voice search.

Speech Recognition

Online Customer Support

Many websites have Online Customer Support services. Let us understand how this happens. A few websites have live executives to answer the queries. Those who do not have Live Executive use ChatBots. Bots search answers from websites and presents them to the reader. This all is possible due to the Machine Learning algorithm. In the time to come, this technique will develop more and can serve the reader in a better way.

Online Customer Support

Online Product Recommendation

If we do online shopping, we often get recommendations of some products. Moreover, we continue to get messages and emails with product recommendations. How does this happen? This is due to the Machine Learning algorithm. ML sends recommendations on the basis of purchase behavior study of the customer, previous experience and taste of customer like price, brand, place, etc.

Fraud Banking

Nowadays, we often come across fraud transactions from banks. Here ML tool helps a lot in detecting fraud in banking. It compares the previous transactions done and if the algorithm finds any different and doubtful transaction, it sends an Alert message immediately. To sum up, this is the most important application of Machine Learning examples.

Search Engine

Google as well as other search engines make use of ML algorithm to present search results. It also analyses the result performance by ML or AL. If the user spends time on its search results, Google assumes its performance as per user interest and keeps a record for the future. If the user doesn’t spend time on the search result, Google records it as non-matching results. This is how Machine Learning helps in search engine performance.

Search Engine

Predictions while Commuting

Most of us use GPS navigation system services. In this way, current locations, as well as velocity, are recorded at a central server. This server prepares a traffic cluster map. Current Ride Apps like OLA or UBER make use of this traffic map to find the traffic congestion and ride price during the ride. Not to mention that all this happens by AL or ML system. If the traffic on the route used is high, the price is more and vice versa. This application is again one of the most important machine learning examples.

Predictions while Commuting

Machine Learning Examples in Farming

Blue River’s “See & Spray” technology uses machine learning to identify plants in farmers’ fields. When the farming is spread over acres of land, it’s difficult to detect plants that require herbicide or if a plant suffering from a particular disease, etc. Here, Machine Learning and Artificial Intelligence come into the picture.  The See & Spray rig can also target specific plants and spray them with herbicide or fertilizer. It’s far more efficient than spraying an entire field and far better for the environment. Farmers can also save time and money.

ml in farming

Self Driving Cars

This is an amazing example of ML. The pilotless cars can sense the obstacle in the front and can take corrective actions. . It can apply brakes or take them aside. This application has a bright future and we will find a large number of such cars on road in years to come.

Self Driving Cars


To sum up, Examples of Machine Learning quoted above are a few. There are tons of such examples and applications in our daily life. It shall change the face of the living standard of human beings in years to come.

The author is an Electrical Engineer by profession and writing articles on AI and ML is a passion. Any suggestion for the improvement of this article shall be highly appreciated.

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