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20 AI Examples and Machine Learning Applications in Real World

The magical touch of mysterious science makes our lives more comfortable and preferable than before. In our everyday life, the contribution of science is just undeniable. We can not overlook or ignore the effect of science on our lives.

Since, at present, we are habituated to the Internet in many steps of our day-to-day life, i.e., to go through an unknown route now we use a Google map, to express our thoughts or feelings use social networks, or to share our knowledge use blogs, to know the news we use online news portals and so on.

If we try to understand the effect of science in our lives precisely, then we will notice that actually, these are the outcomes of using Artificial Intelligence and Machine Learning applications.

In this article, we try to capture the splendid real-time applications of Machine Learning, which will make our perception of life more digital.

Best AI and Machine Learning Applications

Recently, there has been a dramatic surge of interest in the era of Machine Learning, and more people have become aware of the scope of new applications enabled by the Machine Learning approach.

It builds a roadmap to contact the device and makes the device understandable to respond to our instructions and commands. However, the 20 useful applications of Machine Learning are listed here.

1. Image Recognition

Image Recognition is one of the most significant Machine Learning and artificial intelligence examples. It is an approach for identifying and detecting a feature or an object in the digital image. Moreover, this technique can be used for further analysis, such as pattern recognition, face detection, optical character recognition, etc.

image recognition

Though several techniques are available, using a machine learning approach for image recognition is preferable. A machine learning approach for image recognition involves extracting the key features from the image and, therefore, inputting these features into a machine learning model.

2. Sentiment Analysis

Sentiment analysis is another real-time machine learning application. It also refers to opinion mining, sentiment classification, etc. It’s a process of determining the speaker’s or writer’s attitude or opinion. In other words, it’s the process of finding out the emotion from the text.

The main concern of sentiment analysis is “What do other people think?”. Assume that someone writes, “The movie is not so good.” To find out the actual thought or opinion from the text (is it good or bad) is the task of sentiment analysis.

This sentiment analysis application can also be applied to other applications, such as review-based websites and decision-making applications.

sentiment analysis

This machine learning approach is a discipline that constructs a system by extracting knowledge from data. Additionally, this approach can use big data to develop a system. In the machine learning approach, two types of machine learning algorithms are supervised and unsupervised. Both of these can be used for sentiment analysis.

3. News Classification

News classification is another benchmark application of a machine learning approach. Why or How? As a matter of fact, the volume of information on the web has grown tremendously. However, every person has his individual interest or choice. So, picking or gathering appropriate information becomes a challenge to the users from the ocean of this web.

news classification

Providing that interesting category of news to the target readers will surely increase the acceptability of news sites. Moreover, readers or users can search for specific news effectively and efficiently.

There are several methods of machine learning for this purpose, i.e., support vector machine, naive Bayes, k-nearest neighbor, etc. Moreover, there are several “news classification software” available.

4. Video Surveillance

A small video file contains more information than text documents and other media files, such as audio and images. For this reason, extracting useful information from video, i.e., the automated video surveillance system, has become a hot research issue. With this regard, video surveillance is one of the advanced applications of a machine learning approach.

video surveillance

The presence of a human in a different frame of a video is a common scenario. In the security-based application, identification of the human from the videos is an important issue. The face pattern is the most widely used parameter to recognize a person.

A system with the ability to gather information about the presence of the same person in a different frame of a video is highly demanding. There are several methods of machine learning algorithms to track the movement of humans and identify them.  

5. Email Classification and Spam Filtering

To classify email and filter spam in an automatic way a machine learning algorithm is employed. There are many techniques, i.e., multi-layer perception and C4.5 decision tree induction, used to filter spam. Rule-based spam filtering has some drawbacks, whereas spam filtering using the ML approach is more efficient.

6. Speech Recognition

Speech recognition is the process of transforming spoken words into text. It is additionally called automatic speech recognition, computer speech recognition, or speech-to-text. This field has benefited from the machine learning approach and big data advancement.

speech recognition

At present, all commercial-purpose speech recognition systems use a machine learning approach to recognize speech. Why? Using a traditional method, the speech recognition system using the machine learning approach outperforms better than the speech recognition system.

Because, in a machine learning approach, the system is trained before it goes for validation. Basically, the machine learning software of speech recognition works in two learning phases: 1. Before the software purchase (train the software in an independent speaker domain) 2. After the user purchases the software (train the software in a speaker-dependent domain).

This application can also be used for further analysis, i.e., healthcare, educational, and military.

7. Online Fraud Detection

Online fraud detection is an advanced application of a machine learning algorithm. This approach is practical and provides cybersecurity to users efficiently. Recently, PayPal has been using a machine learning and artificial intelligence algorithm for money laundering.

This advanced machine learning and artificial intelligence example helps to reduce the loss and maximize the profit. Using machine learning in this application, the detection system becomes more robust than any other traditional rule-based system.

8. Classification

Classification or categorization is the process of classifying objects or instances into a set of predefined classes. The use of the machine learning approach makes a classifier system more dynamic. The goal of the ML approach is to build a concise model. This approach helps improve the efficiency of a classifier system.

Every instance in a data set used by the machine learning and artificial intelligence algorithm is represented using the same set of features. These instances may have a known label called the supervised machine learning algorithm.

In contrast, if the labels are known, then it’s called unsupervised. These two variations of the machine learning approaches are used for classification problems.

9. Author Identification

With the rapid growth of the Internet, the illegal use of online messages for inappropriate or illegal purposes has become a major concern for society. For this regard, author identification is required.

Author identification is also known as authorship identification. The author identification system may use a variety of fields, such as criminal justice, academia, and anthropology.

Moreover, organizations like Thorn use author identification to help end the circulation of child sexual abuse material on the web and bring justice to a child.

10. Prediction

Prediction is the process of saying something based on the previous history. It can be weather prediction, traffic prediction, and many more. All sorts of forecasts can be done using a machine learning approach. There are several methods, like the Hidden Markov model, that can be used for prediction.

11. Regression

Regression is another application of machine learning. There are several techniques for regression are available.

Suppose X1, X2, X3 ,….Xn are the input variables, and Y is the output. In this case, machine learning technology provides the output (y) based on the input variables (x). A model is used to precise the connection between numerous parameters as below:


Using a machine learning approach in regression, the parameters can be optimized.

12. Services of Social Media

Social media uses the machine learning approach to create attractive and splendid features, i.e., people you may know, suggestions, and reaction options for their users. These features are just an outcome of the machine learning technique.

social media services

Do you ever think of how they use the machine learning approach to engage you in your social account?

For example, Facebook continuously notices your activities, like with whom you chat, your likes, workplace, and study place. And machine learning always acts based on experience. So, Facebook gives you a suggestion based on your activities.

13. Medical Services

Machine learning methods and tools are used extensively in the area of medical-related problems. To detect a disease, therapy planning, medical-related research, and prediction of the disease situation. Using machine learning-based software in healthcare problems brings a breakthrough in our medical science.

14. Recommendation for Products and Services

Suppose that we purchased several things from an online shop several days before. After a couple of days, you will notice that the related shopping websites or services are recommended.

product recommendation

Again, if you search for something on Google, a similar type of thing is recommended for you after your search. This recommendation of products and services is the advanced application of the machine learning technique.

Several machine learning methods, supervised, semi-supervised, unsupervised, and reinforcement, are used to develop these products’ recommendation-based systems. This type of system was also built with the incorporation of big data and machine learning techniques.

15. Online Customer Support

online customer support

Recently, almost all websites allow customers to chat with website representatives. However, no website has an executive. Basically, they develop a chatbot to chat with the customer to know their opinion. This is possible only for the machine learning approach. It’s just the beauty of machine learning algorithms.

16. Age/Gender Identification

Forensic-related tasks have recently become a hot research issue in the world of research. Many researchers are working to bring an effective and efficient system to develop an enriched system.

In this context, age or gender identification is an important task in many cases. Age or gender identification can be made using a machine learning and AI algorithm, i.e., an SVM classifier.  

17. Language Identification

Language identification (Language Guessing) is the process of identifying the type of language. Apache OpenNLP and Apache Tika are the language-identifying software. There are several approaches to identify the language. Among these, the machine learning and artificial intelligence approach are efficient. 

18. Information Retrieval

The most significant machine learning and AI approach is information retrieval. It is the process of extracting the knowledge or structured data from the unstructured data. Since then, the availability of information for web blogs, websites, and social media has grown tremendously.

Information retrieval plays a vital role in the big data sector. In a machine learning approach, a set of unstructured data is taken for input, and the knowledge is extracted from the data.

19. Robot Control

A machine learning algorithm is used in a variety of robot control systems. For instance, recently, several types of research have been working to gain control over stable helicopter flight and helicopter aerobatics.

robot control

A robot driving for over one hundred miles in the desert was won by a machine learning robot that refined its ability to notice distant objects in a Darpa-sponsored competition.

20. Virtual Personal Assistant

A virtual personal assistant is an advanced application of machine learning and artificial intelligence. In the machine learning technique, this system acts as follows: a machine-learning-based system takes input, processes the input, and gives the resultant output. The machine learning approach is important as it acts based on experience.

virtual personal assistant

Different virtual personal assistants are smart speakers of Amazon Echo and Google Home, as well as mobile apps of Google Assistant.

Ending Thoughts

Our expert team has curated a comprehensive list of examples of machine learning and artificial intelligence in today’s life in this article. The main difference between traditional and machine learning-based software is that the system is trained using a large volume of data.

Also, it acts based on experience. So, the machine learning approach is more effective than the traditional approach in problem-solving.

Mehedi Hasan
Mehedi Hasan
Mehedi Hasan is a passionate enthusiast for technology. He admires all things tech and loves to help others understand the fundamentals of Linux, servers, networking, and computer security in an understandable way without overwhelming beginners. His articles are carefully crafted with this goal in mind - making complex topics more accessible.


  1. Interesting read. It covers many of the most common uses. It’s fascinating to see how widely machine learning is being used to make our life easier. And it does have plenty of uses across pretty much every imaginable field.

  2. Thanks for sharing. These top machine learning applications will really help us in understanding machine learning and it’s future scope.

  3. Very useful information. Machine Learning having its application in almost every field and it is useful in our day to day life like product recommendations, traffic prediction while commuting, social media. It is also helpful in bank sectors and healthcare industries.


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