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Mesmerizing examples of Machine Learning

14th April 2020 Print

The process of enabling machines to take action without specific programming is known as machine learning. It has brought us self-driving vehicles, functional speech recognition, successful web search, and a much-enhanced understanding of the human genocopy over the past decade. Today this learning is so common that you use it hundreds of times a day without understanding it. 

Machine learning is one of the technological inventions that has aided man not just to develop many professional and Industrial processes, but also to improve everyday life. But what is learning about machines? This is a branch of artificial intelligence that emphasizes on using statistical techniques to create smart computer systems to learn from available databases. Professional who ventures in this area must pursue a Machine Learning Certification.

Machine learning is currently being used in different fields and enterprises. Medical diagnosis, image processing, estimation, description, correlation with knowledge, and regression are some of the examples. Based on machine learning algorithms, intelligent systems have had the opportunity to learn from previous experience or historical data. Machine learning programs produce predictions based on past experiences. In this article, we'll be addressing ten real-life examples of how machine learning helps to create better technologies to drive the ideas of today.

Image Recognition

This is one of the most regular implementations of machine learning. There are several cases where the object could be identified as a digital photo. For instance, in the case of a white and black image, each pixel's intensity is used as one of the steps. Increasing pixel provides three intensity measurements in three different colors in colored images-red, green and blue (RGB).

This type of learning can also be used on a picture for face detection. In a database of several different people, there is a particular category for each individual. Machine learning can also be used to distinguish both hand-written and published letters for character recognition. We may break a piece of writing into smaller images with a specific character in each.

Speech Recognition

Recognition of speech is the transformation into the text of the spoken words. It is also known as the interpretation of machine voice or automated speech appreciation. A specific software application here can identify the words spoken in an audio clip or film, and later transform the audio into a text file. In this case, the calculation can be a set of numbers describing the speech signal. We could also segment the voice signal in varying time-frequency bands by the levels of intensity.

Apps such as voice user interface, voice queries use speech recognition, and many more. The voice-user interfaces provide call routing, voice dialing, and control of appliances. Basic data entry and the elaboration of standardized records may also be used.

Medical Diagnosis

This learning can also be included in devices as well as in the techniques that can aid in disease diagnostics. This is often used for studying clinical parameters as well as their combination to forecast disease development from the diagnosis example for the drawing out of medical information for outcome testing, patient tracking, and therapy preparation. These are the positive realizations of the methods of machine learning. It can help to incorporate computer-based systems into the healthcare industry.

Statistical Arbitrage

Arbitrage in finance refers to short-term, electronic trading strategies that involve a significant number of securities. Based on variables such as general economic and historical correlations factors, the consumer focuses on applying a trading algorithm for a collection of protection in such strategies. Machine learning techniques are used to achieve an arbitrage strategy for the index. We use Support Vector Machine and linear regression to stock flux values.

Learning Association

Understanding associations are the method of gaining insights into the different relationships between the products. One perfect example is how to connect the unrelated items. One of the machine learning applications is the analysis of the connections between the goods people purchase. If a consumer buys a commodity, similar products are shown to him because the two products are related. Whenever any new items are introduced on the market, they are associated with the old ones to boost their sales.


Classification is a process whereby each person is put understudy in several groups. The method helps evaluate an object's dimensions to classify which group the object belongs to. Analysts use data to create an efficient link. For instance, it examines the customers on their capacity to pay loans before a bank chooses to disperse loans. Each example is expressed to use the same set of characteristics in a set of data used by artificial intelligence and machine learning algorithm. We can do this by analyzing factors such as consumer sales, investments, and financial history. This information was taken from previous loan data.


The prediction systems can also use machine learning. In the example of the loan, the program will have to classify the data available to determine the likelihood of failure. It's specified by a set of standards the analysts prescribe. When the classification is completed, we can measure the fault probability. These computations can be measured for different purposes across all industries. Making predictions is among the best applications for machine learning.

Search Engine and Result Filtering

Google and several other browsers are using machine learning to boost the search terms. The algorithms behind keep an eye on how you react to the results each time you search. If you select the results and remain long on the website page, the browser assumes that the findings displayed correspond to the query. Likewise, if you enter the search results on either the second, third or fourth page and do not view any of the pages, the search engine calculates that the pages presented did not suit the requirement. In this way, the search results are improved by the algorithms that operate at the back end.


Data mining is one of the popular machine learning techniques. This is the method by which structured knowledge is extracted from unstructured data. Web sites, posts, forums, company reports, and emails, for instance. The relational database contains the production generated by the extraction of information. The production process takes several documents as input, and hence the organized data is output.


They can apply machine learning in regression too. In regression, they will use the theory of machine learning to maximize the parameters. This is often used to lower the approximation error and to measure the outcome as near as possible. We may also use this learning to optimize the feature.

Financial Services

Machine learning in the banking and financial industry has much potential. That is the key driver behind all the financial services ' success. Learning machines can help banks and financial institutions make wise decisions. This learning can allow financial services to detect the closing of an account before it occurs. It can also monitor consumer spending patterns. Business research can also be carried out by machine learning. Intelligent machines can be programmed to follow the trends in spending. The algorithms can quickly identify patterns and be able to react in real-time.

In summary, we may conclude that machine learning is an excellent advance in the area of artificial intelligence. There is no doubt that Machine Learning makes people work more creatively and effectively. 

With Machine Learning, you can also assign a very complicated or monotonous task to the machine-beginning with scanning filling and saving paper documents like invoices, to processing and editing images. It also provides a road map for accessing the system and makes this comprehensible to respond to our command and instructions. While machine learning has some scary consequences, its applications are one of the ways that technology can make our lives better.