Home Cloud Computing Best 30 Machine Learning Applications That You Must Know

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 Best 30 Machine Learning Applications That You Must Know.

What is 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 key element 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 online purchase, suggest a topic during searching on a search engine, recommend a friend on your social media and drive a car without human. In our day to day life, we can see the implementation of ML everywhere. Some of the common 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 provide?

  • Personal voice assistance can answer any question if you ask like “hey Google, what time is it now?”
  • You can set alarm with voice assistant
  • If you want to make any to-do list it is easy 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 is utilizing 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, it can tell you which track you should follow. 

How it Works

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

The Google map will type the name of the place and press enter. Now you will get various options like directions, save, nearby, send to phone and share. After setting your home or work location you will find another option like walking, bus, Jeep bicycle, and air. If you sleep 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 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 very simple, it is the application of machine learning.

How 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 calculate 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, application 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 that is 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 then your track is synchronized by the third party YouTube. It will start showing advertisements based on your previous history.

Chatbot: many websites offer chatbot 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 at your spam folder. But the question is how email service providers understand the spam mail? It also happens through the help of a machine learning application. In our previous article, we have seen machine learning have various techniques like supervised and unsupervised machine learning.

Email spam filtering is the ML application of supervised learning. In supervised machine learning all the data set 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 send to the spam folder.

Example of Email filtering

Some of the popular spam filter 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 take place online fraud, for example, fake accounts, identity theft, and man-in-the-middle attacks. it may steal your money where the transaction is taking place. The feed-forward neural network checks your transaction whether it is genuine or fraud transaction.

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 important machine learning application we can see in the stock market. Long 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 the 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 it is there are lesions in the brain.

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

Some of the common 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) applied here for image recognition. the text is identified by optical character reader/ OCR software. On the other hand, the sequence to sequence algorithm is used to translate the text from one language to another.

9. Machine Learning Application in Civil Engineering

Machine Learning Application in Civil EngineeringBeside the computer science, engineering machine learning is applied also 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 Machine Learning is Used in Civil Engineering?

In structural engineering, the ML is used for finding the 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 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 application 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. Classified soil class and time is a problem that can talk with ML and using both visual and sensor data. Its uses by soil classification, feature extraction, segmentation, image preprocessing and image acquisition.

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 within 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 smart 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 is human interference is not possible to machine learning collects data from there. It can drive information from the 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. With your mother tongue, you can speak English, Chinese, Japanese, Spanish and even Gujarati. If you speak any language the application automatically identifies which language it is? Machine learning is doing everything normal.

15. Age/Gender Identification

With then 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 then it provides the output of age, sex, etc.

16. Online Customer Supports

Application of machine learning is also seen in Online Customer Supports. To provide real-time support various online sellers use the ML for live chat, support and automated delivery of products. Suppose you are calling to 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 instruction 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 investing a lot to get the proper output in the sector of machine learning. The application of 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 for 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 a good experience. It can be the best example of regression.

20. Prediction

Machine learning application is used for prediction for in uncertainty. The insurance premium, stock market analysis and many other predictions are done by machine learning. the prediction is done 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 to our outside then the search engine automatically finds that it is 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 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. Beside object recognition, translation, and natural language processing the speech recognition in also developed by machine learning. The deep neural networks and discrimination training model is 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 sent the signal to Human. For the overall system monitoring, you can use any proven IoT application.

25. News Classification

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

26. Sentiment Analysis

Semantic analysis is the basic example of machine learning where it applies the processing of a big 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 the risky and dangerous places, a self-driving car can be sent for test and trial basis. In the desert area, the self-driving car by machine learning is also already tested.

The self-driving car using 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. But how does it work? It is also a machine learning application. 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 applied here to filter, process and classify signals. It is the process by which 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

To develop any project there may be a lot of challenges. 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 the developer bias. So it is less applied for 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 mankind.
  • 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 big 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.

I am also a freelance blogger and real worm of Apps. I love to experiments various apps and games on my android and iOS platform. So here I want to share my cumulative experience and findings regarding various types of apps and games. I am optimistic that this apps review will help the online reader to find the best apps and games for the particular OS.


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