Examples of Machine Learning

Machine Learning technology has widely changed the lifestyle of a human beings as we are highly dependent on this technology. It is the subset of Artificial Intelligence, and we all are using this either knowingly or unknowingly. For example, we use Google Assistant that employs ML concepts, we take help from online customer support, which is also an example of machine learning, and many more.

Machine Learning uses statistical techniques to make a computer more intelligent, which helps to fetch entire business data and utilize it automatically as per requirement. There are so many examples of Machine Learning in real-world, which are as follows:

1. Speech & Image Recognition

Computer Speech Recognition or Automatic Speech Recognition helps to convert speech into text. Many applications convert the live speech into an audio file format and later convert it into a text file.

Voice search, voice dialing, and appliance control are some real-world examples of speech recognition. Alexa and Google Home are the most widely used speech recognition software.

Similar to speech recognition, Image recognition is also the most widely used example of Machine Learning technology that helps identify any object in the form of a digital image. There are some real-world examples of Image recognition, such as,

Tagging the name on any photo as we have seen on Facebook. It is also used in recognizing handwriting by segmenting a single letter into smaller images.

Further, there is the biggest example of Image recognition is facial recognition. We all are using new generation mobile phones, where we use facial recognition techniques to unlock our devices. Hence, it also helps to increase the security of the system.

2. Traffic alerts using Google Map

Google Map is one of the widely used applications whenever anyone goes out to reach the correct destination. The map helps us find the best route or fastest route, traffic, and much more information. But how it provides this information to us? Google map uses different technologies, including machine learning which collects information from different users, analyze that information, update the information, and make predictions. With the help of predictions, it can also tell us the traffic before we start our journey. Machine Learning also helps identify the best and fastest route while we are in traffic using Google Maps. Further, we can also answer some questions like does the route still have traffic? This information and data get stored automatically in the database, which Machine Learning uses for the exact information for other people in traffic. Further, Google maps also help find locations like a hotel, mall, restaurant, cinema hall, buses, etc.

3. Chatbot (Online Customer Support)

A chatbot is the most widely used software in every industry like banking, Medical, education, health, etc. You can see chatbots in any banking application for quick online support to customers. These chatbots also work on the concepts of Machine Learning. The programmers feed some basic questions and answers based on the frequently asked queries. So, whenever a customer asks a query, the chatbot recognizes the question's keywords from a database and then provides appropriate resolution to the customer. This helps to make quick and fast customer service facilities to customers.

4. Google Translation

Suppose you work on an international banking project like French, German, etc., but you only know English. In that case, this will be a very panic moment for you because you can't proceed further without reviewing documents. Google Translator software helps to translate any language into the desired language. So, in this way, you can convert French, German, etc., into English, Hindi, or any other language. This makes the job of different sectors very easy as a user can work on any country's project hassle-free.

Google uses the Google Neural Machine Translation to detect any language and translate it into any desired language.

5. Prediction

Prediction system also uses Machine learning algorithms for making predictions. There are various sectors where predictions are used. For example, in bank loan systems, error probability can be determined using predictions with machine learning. For this, the available data are classified into different groups with the set of rules provided by analysts, and once the classification is done, the error probability is predicted.

6. Extraction

One of the best examples of machine learning is the extraction of information. In this process, structured data is extracted from unstructured data, and which is used in predictive analytics tools. The data is usually found in a raw or unstructured form that is not useful, and to make it useful, the extraction process is used. Some real-world examples of extraction are:

• Generating a model to predict vocal cord disorders.
• Helping diagnosis and treatment of problem faster.

7. Statistical Arbitrage

Arbitrage is an automated trading process, which is used in the finance industry to manage a large volume of securities. The process uses a trading algorithm to analyze a set of securities using economic variables and correlations. Some examples of statistical arbitrage are as follows:

• Algorithmic trading that analyses a market microstructure
• Analyze large data sets
• Identify real-time arbitrage opportunities
• Machine learning optimizes the arbitrage strategy to enhance results.

8. Auto-Friend Tagging Suggestion

One of the popular examples of machine learning is the Auto-friend tagging suggestions feature by Facebook. Whenever we upload a new picture on Facebook with friends, it suggests to tag the friends and automatically provides the names. Facebook does it by using DeepFace, which is a facial recognition system created by Facebook. It identifies the faces and images also.

9. Self-driving cars

The future of the automobile industry is self-driving cars. These are driverless cars, which are based on concepts of deep learning and machine learning. Some commonly used machine learning algorithms in self-driving cars are Scale-invariant feature transform (SIFT), AdaBoost, TextonBoost, YOLO(You only look once).

10. Ads Recommendation

Nowadays, most people spend multiple hours on google or the internet surfing. And while working on any webpage or website, they get multiples ads on each page. But these ads are different for each user even when two users are using the same internet and on the same location. These ads recommendations are done with the help of machine learning algorithms. These ads recommendations are based on the search history of each user. For example, if one user searches for the Shirt on Amazon or any other e-commerce website, he will get start ads recommendation of shirts after some time.

11. Video Surveillance

Video Surveillance is an advanced application of AI and machine learning, which can detect any crime before it happens. It is much efficient than observed by a human because it is a much difficult and boring task for a human to keep monitoring multiple videos; that's why machines are the better option. Video surveillance is very useful as they keep looking for specific behavior of people like standing motionless for a long time, stumbling, or napping on benches, etc. Whenever the surveillance system finds any unusual activity, it alerts the respective team, which can stop or help avoid some mishappening at that place.

Some popular uses of video surveillance are:

• Facility protections
• Operation monitoring
• Parking lots
• Traffic monitoring
• Shopping patterns

12. Email & spam filtering

Emails are filtered automatically when we receive any new email, and it is also an example of machine learning. We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail:

• Content Filter
• Header filter
• General blacklists filter
• Rules-based filters
• Permission filters

Some machine learning algorithms that are used in email spam filtering and malware detection are Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier.

13. Real-Time Dynamic Pricing

Whenever we book an Uber in peak office hours in the morning or evening, we get a difference in prices compared to normal hours. The prices are hiked due to surge prices applied by companies whenever demand is high. But how these surge prices are determined & applied by companies. So, the technologies behind this are AI and machine learning. These technologies solve two main business queries, which are

• The reaction of customers on surge prices
• Suggesting optimum prices so that no harm of customer losing occurs to business.

Machine Learning technology also helps in finding discounted prices, best prices, promotional prices, etc., for each customer.

14. Gaming and Education

Machine learning technology is widely being used in gaming and education. There are various gaming and learning apps that are using AI and Machine learning. Among these apps, Duolingo is a free language learning app, which is designed in a fun and interactive way. While using this app, people feel like playing a game on the phone.

It collects data from the user's answer and creates a statical model to determine that how long a person can remember the word, and before requiring a refresher, it provides that information.

15. Virtual Assistants

Virtual assistants are much popular in today's world, which are the smart software embedded in smartphones or laptops. These assistants work as personal assistants and assist in searching for information that is asked over voice. A virtual assistant understands human language or natural language voice commands and performs the task for that user. Some examples of virtual assistants are Siri, Alexa, Google, Cortana, etc. To start working with these virtual assistants, first, they need to be activated, and then we can ask anything, and they will answer it. For example, "What's the date today?", "Tell me a joke", and many more. The technologies used behind Virtual assistants are AI, machine learning, natural language processing, etc. Machine learning algorithms collect and analyze the data based on the previous involvement of the user and predict data as per the user preferences.