11 Machine Learning Examples in Real Life
“2021 is the time of making smart decisions, to believe in a leap of faith. This is the time of Machine Learning.”
Over a decade before, we mainly relied on traditional and cumbersome methods to perform our tasks.
But now, to get accurate and instant results on time, ML (machine learning) uses stats and predicts any assumptions based on history, to provide better results and transformation to our lives.
In this blog, we are going to understand real-world examples of machine learning and much more.
Let’s get started!
Best Real-World Examples of Machine Learning
1. Image Recognition
Our minds perfectly know and understand everything. When we look at the images, our mind starts processing them and stores the information in the subconscious part for a long time.
But with machines like computers, it’s hard to understand things. Data is in raw form and needs to be processed with advanced algorithms.
Here comes the role of machine learning technology and its tactics.
ML algorithms are supervised, fragment the image into smaller pixels or parts to the process. Based on the intensity of the pixel or its colour, the computer identifies the image.
Although this could take a large amount of data. This is important to find the right image from the bulk of images.
Google Lens is the best example of Image recognition, the mobile scanner is another such example.
Handwritten notes scan.
Photograph face identification using filters like name, place, etc.
Self-driving cars identify any obstacle image using ML.
Drones hit the right target using this technique.
And so on.
2. Voice Assistant
Voice assistant is one of the best examples of Machine Learning. For example, Google does virtual assistance using the voice-recognition technique. This virtual tool is smart in predicting the correct voice of humans.
You can even perform your daily activities via this assistant. If you want to wake up in the early morning at 4 or want to visit your favourite place next time or buy some food groceries from the nearest market, Google Assistance is the all-in-one place that helps you to navigate and perform your activity at the earliest.
ML monitors every activity and assures the best and accurate result on time. The speech is broken down by intensities of different time bands.
Some other examples to use the smart assistants are Siri, Alexa, Echo, Google Home, and others. In addition, smart TVs like Samsung, and Xiaomi, run virtual assistants in parallel to perform daily activities.
3. Predictive Analysis
This is also known as sentiment analysis. Evolves prediction of sentiments, or emotions, by breaking each part and process via the data mining algorithms.
Data mining is part of Machine Learning, which requires the extraction of data into smaller bits to identify the emotions like positivity, negativity, or neutral emotion.
Apps like Saavn, Wynk, Hungama, and many others are switching to ML to suggest better recommendations and lists to their audiences.
ML has a brain within, which collects the information and analyzes the interest and behaviour of the user. Based on that, the app showcases listening list suggestions and much more to the user for a great experience.
Enhanced the customer listening experience.
Effective brand socializing.
Better monitoring of feedback and history of audiences.
4. Medical Diagnosis
Detecting the stock of medicines at an early, automatic schedule of medicines to the medical vendors.
Track the health data of the patient and record it to prepare the prescription in case of revisiting the patient for the same medicines.
Machine Learning helps to resolve the issue of dealing with any partial data, helps to manage the continuous data flown by intensive care units, and results in efficient solutions to the patients.
Early prediction of any disease
Plan quick support system for the emergency patient
Well-Manage the patient and doctor records
5. Traffic Alerts
Gone are the days when we used to be stuck in the middle of a place that led to any path. For that reason, we had to wait in the traffic, with no other better option.
But now times have changed, new features like Google Maps help their users to plan better routes with less traffic, suggest the peak hours to avoid going anywhere outside, suggest the arrival of buses, and many more things.
It is possible with the intelligent GPS technology of Google Maps, to collect the historic data of the users, manage the traffic, save our last location, suggest you travel at which speed to reach a particular destination, etc.
In addition, another feature using Machine Learning is that travelling apps are built with the feature to play and listen to your favourite music without even switching to any app.
ML development services like Ola, Uber, predict the price of the route travelled by the user earlier and ETA at the booking time.
6. Video Surveillance
This technology is used intelligently. In the past, the military used surveillance to monitor any activity operated in some other part of the country.
But now this technology is used in day-to-day life for monitoring any mischievous activity run in the back.
Machine Learning-based devices collect the data as an object and predict better results for surveillance. The training and mining process of the data is used to obtain accurate results and target the image.
For security and alarming situations, surveillance is important. Other purposes for video surveillance are:
Predict any abnormal activity or event.
And many others.
7. Chatbots for Support
Every time you visit any app like social media apps, banking apps or food apps, or any kind of fitness app, you must have noticed that there is an option called to ‘chat with us’. Many of us have tried and asked their query on it.
Some time ago this might sound weird to talk with a bot, but now it’s a must-have type of assistant to resolve our query.
Bots do their answers using the intelligent NLP algorithms and decision trees, which come under Machine Language.
ML development company uses bots as your assistance, to analyze your queries, and predict the relevant answer. Bots have trained with millions of queries before being placed at the app interface.
8. Google Translator
Everyone is not proficient enough to understand different languages. There are times when we need to travel alone with only knowledge of our mother tongue. In such a situation we might get a puzzle to recognize the language of that particular place.
Google Translator is a saviour for us in such crucial times when we are short of time and have no one to understand the language. It has super NLP (natural language processing) algorithms to translate the language into small segments and then process it into human-understandable language.
Translation involves the ability to understand words, dictionaries, languages, or detect the languages automatically.
9. Stock Market Analysis
Machine Learning has a decent place among various domains, so why not inside the stock market.
Using ML, investors and market experts can understand the shift curve of the market and can make the best decision.
In the past, fake traders limited the growth of the market and hence devastated the market economy to the landfills.
But now ML algorithms have been introduced to secure the financial risk of the investors so that they can take instant and accurate results for their money.
The graph trends and patterns give meaningful outcomes to the traders and the investors.
10. Self-Driving Cars
You have heard the name of Tesla, a self-driving car, which is featured on ML. Smart driving cars are the urge of today’s time. This is the infusion of many technologies like deep learning, machine learning, artificial intelligence, GPS automation, and others for the autonomous driving of the car.
The most common algorithms for driving hassle-free rides without human intervention are:
Scale-invariant feature transform(SIFT)
You only look once (YOLO)
Self-driving cars, the future of the Gen-Z people. It will be going to change the way of travelling.
11. Real-Time Pricing
IRCTC apps have introduced the real-time/dynamic pricing facility to minimize the cancellation of tickets.
At the time of occasions, flight ticketing companies and apps use surge pricing models to bridge the gap between demand and available facilities.
The machine learning service provider understands the ethics and comes up with a list of factors that affect the fare. Some of them are weather, high demand, occasion, competition of the market, and local issues.
But now one question has been raised: what techniques these companies have used to display dynamic fare.
The answer is simple, AI, ML, and data analysis together help to offer the best prices to the users and companies.
On concluding things from beginning to bottom, Machine Learning is playing the role of the catalyst with a combination of other technologies too. That promises each of us a transforming future.
ML technology offers the facility to everyone including companies, scientists, technocrats, students, and faculty.
Also, Read How To Use Machine Learning for Ecommerce
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