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Applications of Machine Learning Uses in Everyday Life

Applications of Machine Learning

Popular Applications of Machine Learning in Everyday Life

Interest in machine learning for use in various domains is increasing as the amount of data available increases over time. Machine learning offers several techniques to extract knowledge from data that can remain translated into actionable goals.

ML algorithms can enhance field information and automated functions mainly related to regulation and optimization. In addition, machine learning and computer vision have expanded many fields, including medical diagnosis, statistical data analysis, algorithms, scientific research, etc. Moreover, such practices have remained in smartphone applications, computer devices, online websites, cyber security, etc.

Augmented data dominates multiple fields today. Extracting interference and valuable insights from data has emerged as the latest model for both scientific research and commercial applications. In this blog, we will appear at some machine learning applications implemented in our daily practice.

Application of Machine Learning in Everyday Life

Application of Machine Learning in Everyday Life

1. Estimated Commute

In general, one journey takes longer than the average time to complete. Multiple modes of transport remain used for the journey, including the vehicle’s timing to reach the destination. Reducing your commute time isn’t accessible yet. See below how machine learning is helping to reduce your commute time,

Google’s Map: Using location data from smartphones, Google Maps can check the agility of traffic movement at any time. In addition, the map can organize user-reported traffic such as construction, traffic, and accidents. With access to relevant data and relevant power algorithms, Google Maps can reduce commuting times by marking the fastest route.

Ride-hailing applications: From how to price a ride and minimize waiting time to how ride-hailing cars adjust the journey with other passengers to reduce the possibility of detours. Yes, machine learning is the solution. ML helps the company estimate the price of the ride, calculate the optimal pick-up location and ensure the shortest route, also for fraud detection. For example, Uber uses machine learning to optimize its services.

Commercial flights to use Autopilot: With AI technology’s help, flights remain now handled by autopilots. In a report by The NewYork Times, the pilots said they were flying manually for seven minutes, mainly during takeoff and landing, and the rest of the flight was on autopilot.

2. Email Newsletters

Spam filters: Some rule-based filters are not actively displayed in the mailbox, for example, when a message comes with the words “online advice,” “online pharmacy,” or “unknown address.”

ML offers a powerful feature that filters emails from various signals, such as words in the message and metadata (e.g., who sent the news, and where it remained sent from). In addition, it filters emails based on “daily deals” or “welcome messages,” etc.

Email Classification: Gmail categorizes emails into Primary, Promotional, Social, and Updates and marks emails as necessary.

Intelligent replies: You must have noticed how Gmail prompts you to reply to emails with simple phrases like “Thanks,” “Okay,” and “Yes, I’m interested.” These responses remain tailored to the email as ML and AI understand, estimate, and reason how a person positions over time.

3. Banking and Personal Finance

Fraud prevention: In most cases, the daily transaction data is so high in volume that it becomes difficult for people to manually check each transaction and then find out if the transaction is fraudulent.

To solve this problem, artificial intelligence-based systems remain designed to detect what types of fraudulent transactions.

Credit Decisions: When relating to credit cards or loans, financial authorities must quickly decide whether to accept or not. And if you buy the proposal, what could be the specific terms that can remain offered in terms of interest rate, amount of credit line, etc.?

Financial institutions deploy ML algorithms to create credit decisions and independently determine specific risk assessments for users.

Mobile Deposit Check: In addition, AI technology has made mobile banking personalized and valuable for those who don’t have time to visit banks. For example, banks offer the ability to present checks through a smartphone app, negating the need for users to deliver the statement to the bank.

Most banks use technology developed by Mitek to interpret and transform handwriting on checks into text using optical character recognition.

4. Assessment and Evaluation

When checking for plagiarism: ML can be used to create a plagiarism detector. In addition, many schools and universities require plagiarism checkers to analyze students’ writing skills. The algorithmic essence of plagiarism is similarity functions that lead to a numerical estimate of how similar two documents are.

Robo-readers: Essay grading used to be very complex, but now researchers and organizations are creating artificial intelligence systems to grade essays—the GRE scores essays through one human reader and one Robo-reader, known as an e-Rater.

If the grade varies significantly, a second human reader remains considered to make up the difference. (You can go to the article to learn how Robo-readers work).

Shortly, uniform classrooms will remain replaced by personalized and flexible learning that will individually shape each student’s strengths and weaknesses. ML also helps identify at-risk students early so that schools can pay attention to them by providing them with additional learning resources and reducing dropout rates.

5. Social Networks

Facebook: When you upload a photograph to Facebook, it automatically reflects faces and suggests friend tags. Facebook uses AI and ML to classify faces. In addition

It uses an ANN algorithm and powerful facial recognition software to mimic the human brain.

Facebook uses AI to personalize the newsfeed, ensuring it reflects posts people enjoy.

Shows specific business ads that are relevant to their interests.

Pinterest: Uses computer vision to recognize objects in images, or “pins,” and recommends similar pins. Other applications cover spam prevention, search, and discovery, email marketing, ad performance, etc., with the help of machine learning.

Snapchat: Offers face filters (known as Lenses) that filter and track facial activity, allowing users to tag animated images or digital masks that shift when their face moves.

Instagram: Using ML algorithms to identify feelings behind emoticons. Instagram can create and automatically recommend emojis and emoji hashtags. Emojis are used massively across all demographics, which Instagram uses to describe and explore on a massive scale by translating emojis into text.


It is incredible how machine learning and artificial intelligence have changed our lives by making them easier; additionally, with some AI and ML trends, we expect more growth in technology. We have screened various applications here; machine learning is used in the arena to impact our daily lives; it also allows us to make business decisions, optimize operations, and augment productivity for industries to stand out in the market.

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