Best 30 Machine Learning Applications That You Must Know

Machine learning is magic in the real world. We have seen the charisma of that magic everywhere in our daily life. The application of machine learning is from email validation to robotic engineering. It has enriched the system of artificial intelligence. Now a day’s you need not be a data scientist or a machine learning engineer. As ordinary people, you can see the application of machine learning. So, my endeavor for today is narrating the Best 30 Machine Learning Applications That You Must Know.

What are Machine Learning Applications?

Machine learning application is the implication of ML to work as a human without the interference of humans. ML can be applied by both supervised and unsupervised machine learning techniques. When we applied machine learning with a predefined data set, we called it supervised learning. On the other hand, when input is not predefined, we consider it unsupervised machine learning. However, machine learning application is one of the critical elements of online marketing, purchasing, digital payment, fraud detection, email filtering, and product recommendation.

Best 30 Application of Machine Learning

Best 30 Application of Machine LearningMachine learning is applied to check the spam mails, recommend a product during an online purchase, suggest a topic during searching on a search engine, recommend a friend on your social media and drive a car without a human. In our day-to-day life, we can see the implementation of ML everywhere. Some of the standard machine learning applications are:

1. Virtual Personal Assistant

Virtual Personal AssistantA virtual personal assistant is one of the best examples of machine learning applications. It is the independent contractor to provide client service outside of the client’s office. It works based on the individual command or any question. The online chat or chatbot can be compared with a virtual personal assistant. 

Example of a virtual assistant

  • Google Assistant: hay Google/ ok Google
  • Amazon: Alexa
  • Microsoft: Cortana

Devices of Virtual personal assistant

  • Amazon smart speaker
  • Amazon echo
  • Google home
  • Apple homepod
  • Facebook’s M when you are chatting
  • Apple devices ( Siri)
  • Microsoft Windows operating system
  • Samsung smartphone
  • Bixby
  • Wearable technology and
  • Interactive voice response

What are the services personal voice assistance provides?

  • Personal voice assistance can answer any question if you ask, like, “Hey Google, what time is it now?”
  • You can set the alarm with a voice assistant.
  • If you want to make a to-do list, it is accessible by the voice assistant.
  • Suppose you are getting bored you need to listen to some music then ask for your voice assistant.
  • Play videos, movies, live streaming, etc., for example, Netflix and YouTube.
  • Sometimes it assists public interaction with the government.
  • It is also workable for the call center for the set questions.

2. Traffic Prediction as Machine Learning Applications

Traffic Prediction MLMachine learning utilizes the traffic volume of the road. I can give you accurate information to bypass the traffic. With the help of GPS navigation and map integration, can tell you which track you should follow. 

How it Works

Suppose you want to visit a place then what will you do? If the place’s track is familiar to you, then you should search on any of the map service providers. For example, we are using the Google Maps service.

The Google map will type the name of the place and press enter. You will get various options like directions, save, nearby, send to phone, and share. After setting your home or work location, you will find other options like walking, bus, Jeep bicycle, and air. If you sleep in the driving mode, the Google map fully shows you various options to reach that location.

In the image, you can see various combinations of color codes from your location to your destination. The green color indicates that you can go there in any traffic jams. The yellow color represents there is a little bit slower to reach your destination. The red-colored area is a crucial area where you will face traffic jams. Now the question is how Google knows your locality? The answer is straightforward, and it is the application of machine learning.

How does Google apply here to tell you about the traffic jam condition?

Google maps predict whether the traffic is clear, slow-moving, or heavily consisted based on two specific measures:

  • The average time took: Google map calculated based on a specific day at a specific time on that route.
  • Real-time location: It also finds the data of vehicles from Google maps, applications, and sensors.
  • Some of the Google Maps alternatives are Bing MapsMapQuest, OpenStreetMap, GIS Cloud, Here WeGo, Géoportail, Foursquare, Yandex. Maps and Mapbox.

3. Social Media Personalization

Social media always keeps its eyes on you regarding your choice, teste, and likings. When you see an advertisement for a long time, you think any product for learning the details automatically every comment you the relevant product based on your choice. Which is the best application of machine learning to focus on your likings?

Various applications of social media

Facebook: machine learning finds out the relevant product on Facebook. When you click for a product and study for a while, it automatically read your choice. Somethings go when you see any video advertisement on Facebook. After some time, you will find the product advertisement you are watching similar to your choice.

YouTube: YouTube also shows you some advertisements when you’re enjoying your videos. Which of the advertisement is for a specific period. You can also skip the advertisement after a few seconds. If you see the full advertisement or click any of them, your track is synchronized by the third party YouTube. It will start showing advertisements based on your previous history.

Chatbot: many websites offer chatbots to communicate with visitors to the website. By tracking you for taking some input from you, it can suggest or recommend their products for selling.

Personalized video: when you open any video sites like Facebook, Instagram, and Websites, you will see all the videos are based on your likings. YouTube Netflix and other video service providers recovered your videos by using machine learning.

4. Machine Learning Applications: Email Spam Filtering

Email Spam FilteringWhen you open your mail, you can see a lot of emails are in your spam folder. But the question is, how do email service providers understand spam mail? It also happens through the help of a machine learning application. In our previous article, we have seen that machine learning has various techniques like supervised and unsupervised.

Email spam filtering is the ML application of supervised learning. In supervised machine learning, all the data sets are predefined and labeled. Usually, the spam mail has some criteria like the heading contains free, lottery, boost, magic add coupon. Machine learning automatically finds them and sends them to the spam folder.

Example of Email filtering

Some of the popular spam filters used by Gmail are:

  • Header filter
  • Content filter
  • General Blacklist filter
  • Various rules-based filter
  • Permission filter and
  • Challenge-response filter

5. Online Fraud Detection

There are many ways to place online fraud, such as fake accounts, identity theft, and man-in-the-middle attacks. IThe feed-forward neural network checks your transaction, whether it is a genuine or fraud transaction. It may steal your money where the transaction is taking place.

The function of the feed-forward neural network is to convert the output into hass values. Again these values become input for the next term. So every year transaction has some uniqueness, but the fraudulent transaction is identified by the hass values.

6. Stock Market Trading

And the critical machine learning application we can see in the stock market. Extended short-term memory neural network is used here. It is used to classify the process and identify the time duration. It is used to predict stock market trading.

7. Assistive Medical Technology

Assistive Medical TechnologyMedical technology has innovated with the use of machine learning to diagnose diseases. Now we can use the tricky model to identify the diseases. Now it is easy to analyze a 2D CT scan and come up with 3D models that predict where there are lesions in the brain.

Machine learning works equally well for brain tumor and ischemic stroke lesions. It is used further for fetal imaging and cardiac analysis too.

Some of the standard fields where machine learning has contributed are:

  • Radiology
  • Clinical research
  • Drug Discovery
  • Personalized treatment
  • Diseases Identifications

8. Automatic Translation

Suppose you have visited any foreign countries like China or Japan. You cannot read, speak or write Chinese/ Japanese language. With the application of machine learning, you can convert any written image into your desired language.

How does automatic Translation works?

The technology behind this is sequence-to-sequence learning which is the same algorithm used in the chatbot. The convolutional neural networks ( CNN) are applied here for image recognition. The text is identified by optical character reader/ OCR software. On the other hand, the sequence to sequence algorithm translates the text from one language to another.

9. Machine Learning Application in Civil Engineering

Machine Learning Application in Civil EngineeringBesides computer science, engineering machine learning is also applied in the sector of civil engineering. It is applied for its structural engineering, geotechnical engineering, construction management, and material science. Here the civil engineers use machine learning to predict their requirements and optimize at the highest level.

How is Machine Learning Used in Civil Engineering?

In structural engineering, the ML is used for finding damage detection and localization. Some of the applications are:

Dermis detection: By using the visual or sensor data, it detects whether the structure is damaged or not.

Damage localization: machine learning locates the damage in a structure.

Damage quantification: with the help of an analytical and mathematical model, it detects the amount of damage to your structure.

Structural health: machine learning is applied for real-time structural health monitoring. So that it can give a warning for required repair or evacuations, Burj Khalifa is using ML for the same purpose.

Some Machine learning applications in civil engineering are:

  • Construction activity monitoring system
  • Predicting the change of a rate of material
  • Risk prediction and management systems
  • Machine learning for a preliminary estimation
  • Site layout using machine learning
  • A concrete mix design is also applied by ML.

10. Soil Classification

Andhra application of machine learning in soil classification. Its uses by soil classification, feature extraction, segmentation, image preprocessing, and image acquisition. Classified soil class and time are problems that can talk with ML using both visual and sensor data.

11. Machine Learning Applications in Images Recognition

Images RecognitionAny of your friends posted your picture on Facebook without informing you. Facebook sends you a notification for the image posting. But, the question is how Facebook knows that you are the right person for sending your notification. Facebook applied this year to machine learning. It is one of the best approaches to identify and detect a feature of an image or object.

Image recognition is also used for pattern recognition, optical character recognition, face recognition, and detection. The accuracy of facebook is 98% which is near to humans. The value of image recognition was 15.95 Billion dollars, and by 2021 it will be 38.92 billion dollars.

12. Robot Control

Robot Control MLNowadays, research work is going on based on machine learning. We can see the robots are working in the industrial area. Much Medical surgery is also done by intelligent robots. With the help of artificial intelligence and machine learning robot can be controlled like a human.

13. Information Retrieval

Machine learning automatically retrieves information from a set of data. So we can tell it is widely used for data mining. In the critical scenario or location where human interference is not possible, machine learning collects data from there. It can drive information from a large volume of data. It also helps you with big data migration.

14. Language Identification

Now you can travel all over the world without knowing any specific language. You can speak English, Chinese, Japanese, Spanish, and even Gujarati in your mother tongue. If you speak any language, the application automatically identifies which language it is? Machine learning is doing everything normal.

15. Age/Gender Identification

With the application of supervised machine learning, the system can automatically identify your age, sex, and gender. A predefined data is stored on the computer regarding the use case scenario. When it is matched with the computer, it provides the output of age, sex, etc.

16. Online Customer Supports

The application of machine learning is also seen in Online Customer Supports. Various online sellers use the ML for live chat, support, and automated delivery of products to provide real-time support. Suppose you are calling a customer service center. The automated voice response service will receive your phone call and welcome you. For the Hindi language Press 1, for English Press 2. If you press 2, you will listen to instructions in English. This is one of the best examples of machine learning.

17. Machine Learning Applications in Medical Services

It is an advanced-level application of machine learning. The research was investing a lot to get the proper output in the sector of machine learning. Applying robotics in the medical sector based on machine learning is the best example of medical service. Much critical surgery is done by robots with the help of machine learning. It Automatically prescribes you medicine based on your symptoms.

18. Recommendation for Products and Services

When you purchase a product from Amazon or eBay, you get more recommendations based on your taste preference and choice. Here we can see the machine learning application. You are searching for a product but the system recommending colorful products similar to your one. With the help of browser machine learning, capture the data to guide your choice.

19. Regression

The reduction in the relationship between each other variable. Each of the variables depends on the other variable. Here one variable is dependent, and another is independent. If you want to get a good salary, you need good experience. It can be the best example of regression.

20. Prediction

Machine learning application is used for prediction for uncertainty. The insurance premium, stock market analysis and many other predictions are made by machine learning. The prediction is made with the help of a mathematical model of machine learning. Show the possibility of error is very low.

21. Machine Learning Applications in Author Identification

The word plagiarism is very common to us. When we copy anyone’s content and publish it to our outside, the search engine automatically finds a copy component. Much Online plagiarism software finds out the author of the original continent application of machine learning.

22. Classification

When there is the possibility of two answers like true or false, male/female, we use the classification machine learning model. In the classification method, the answer is fixed. In the email, your mail can be sent to a spam box or inbox. It is decided based on classification.

23. Machine Learning Applications in Speech Recognition

ML Applications in Speech RecognitionMachine learning is part of artificial intelligence. Besides object recognition, translation, and natural language processing, speech recognition is also developed by machine learning. The deep neural networks and discrimination training model are used for identifying the speech. The mobile lock is open by speech is another example of machine learning.

24. Video Surveillance

Machine learning and artificial intelligence are using video surveillance systems. Any IP camera is used for capturing the video of a particular place. Machine learning analyzes the video, image, and audio and provides feedback to the system owner. 

The video surveillance systems alert the security system if there is any irregularity. In the real-time system monitoring process, it sends the signal to humans. For the overall system monitoring, you can use any proven IoT application.

25. News Classification

Much big newspaper portal uses machine learning for news classification. Using k nearest neighbor and support vector machine learning model categorizes the newspaper content for the riders. Unknown news is also categorized based on the keyword’s weight and density. Each of the different processes and categorized in a matrix.

26. Sentiment Analysis

Semantic analysis is the primary example of machine learning, where it applies the processing of a significant volume of data. It is utilized for monitoring social media. The Naive Bayes and support vector machine models are used for applying sentiment analysis.

27. Language Translate

Google translator or bing translate can convert your language to any other language based on a machine learning mechanism. With the Application of machine learning, it automatically detects which language it is. It also can translate your language into your desired language.

28. Machine Learning Applications in Self Driving Car

Machine Learning Applications in Self Driving CarA mission running application can be seen for driving the car. In risky and dangerous places, a self-driving car can be sent for a test and trial basis. In the desert area, the self-driving car by machine learning is also already tested.

The self-driving car uses a machine learning neural network. It accomplishes the task using deep neural networks or DNA. With the help of artificial intelligence and reinforcement learning, it drives the car.

29. Application of Machine Learning in Netflix Recommendation

All of us know about the Netflix Recommendation engine. It is also a machine learning application. But how does it work? Netflix makes a matrix based on your likings. The matrix depends on high-probability click-thru image thumbnails, continuously A/B tests throughout its user base, and time spent on the videos.

30. Machine Learning Applications Signal Processing

Signal Processing is both science and art to modify the data based on time series to enhance or further analysis. Machine learning is applied here to filter, process, and classify signals. It is how computers learn the pattern of voice, tone, speech, and different classes.

If you want to implement a lie detection system, you have to rely on ML. Where humans fail to detect the original time there the machine finds out the original owner. This sector is still under research. With the help of AI, it will flourish a lot.

Machine-learning Application’s Challenges

There may be a lot of challenges to develop any project. Those challenges are a hindrance to developing a machine learning project. Some of the challenges are mentioned below.

  • Some people use machine learning for bad intentions. The subject goes against ethics.
  • Sometimes the machine learning is used only for developer bias. So it is less applied to society.
  • The system parameters are not always followed by ethics.
  • Machine learning has reduced the employment of humans. So unemployment it maybe increases higher.
  • If the correlation becomes false, the program will be poisonous.
  • Contaminated or poisonous data may be harmful to humanity.
  • You may face the problem of data engineering, cleaning, features, extraction in the application of machine learning.
  • The job of machine learning is based on prediction. Show some time the result maybe not as per expectation.

Final Thought

In this great article, we have tried to give a comprehensive idea of the application of machine learning. In this article, we have seen that machine learning is using from your bed to grave. It has become mass most popular for preventing fraudulent transactions, image recognization, preventing spam mail, and many other days to day life. We also can see the machine learning application for predicting insurance premiums, virtual personal assistant language identification, and recommendations for products and services. Day by day, the utilization of machine learning will interest, and the quantity of machine learning engineers will also be increased on a large scale.

Hawlader's passion for technology has driven him to be an avid writer for over 16 years. His vast knowledge of the Windows and Android operating systems is a testament to his proficiency in the field. In addition to his expertise in open source software, he also possesses an extensive understanding of the open-source platform, making him a valuable resource for technology enthusiasts. His contributions to FossGuru writers with research-based articles have helped readers to stay up-to-date with the latest trends in the tech industry. Furthermore, Hawlader's curiosity for scientific breakthroughs has led him to be a keen reader of science blogs, keeping him informed about the latest developments in the field.

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