Applications & Uses of Machine Learning

Applications & Uses of Machine Learning

Applications & Uses of Machine Learning

Machine learning is used to develop algorithms capable of obtaining input data and using statistical analysis to predict output based on the type of available data. These machine learning algorithms are categorized as supervised, unsupervised and reinforced learning where all of these algorithms have numerous infinite applications such as Image & Voice Recognition, Video Surveillance, Social Media Network, Spam and Malware Detection, Customer Service, Search Engine, Fraud Detection and Prediction etc

Machine Learning Uses

Machine learning implementations are infinite and there are many machine learning algorithms available to learning. They are available from simple to highly complex, in every form.

Top 10 Machine-learning uses are:

1.    Image Recognition

It is one of the most popular applications for the machine learning. It can also be referred to as a digital image, and the calculation defines the output of each pixel in an image for such images. Face recognition is also one of the great features that pure machine learning has created. It helps people identify the face and submit the associated updates.

In the case of a black and white image, the intensity of each pixel serves as one measurement. So if a black and white image has N*N pixels, the total number of pixels and measurement is N2.

In the coloured image, each pixel considered as providing 3 measurements of the intensities of 3 main colour components ie RGB. So N*N coloured image there are 3 N2 measurements.

  • For face detection –The categories might be face versus no face present. There might be a separate category for each person in a database of several individuals.
  • For character recognition– We can segment a piece of writing into smaller images, each containing a single character.  The categories might consist of the 26 letters of the English alphabet, the 10 digits, and some special characters.

2.    Voice Recognition

Speech recognition (SR) is the translation of spoken words into text. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT). Machine learning ( ML) helps also to improve the voice recognition program. It also known as Virtual Personal Assistants (VPA). It will help you to find the information when asked over the voice. After your question, that assistant will look for the data or the information you have asked and gather the information you need to give you the best answer. In today’s world of machine learning, there are several tools available for voice recognition that is the Amazon echo and the smart speakers are the home googles. There is a mobile app called Google allo, and the Samsung S8 and Bixby smartphones.

3.  Medical Diagnosis

ML offers processes, procedures, and tools that can help in a range of medical fields to solve diagnostic and prognostic problems. This is used to evaluate the value of clinical parameters and their combinations for prognosis, e.g. for predicting disease progression, for extracting medical knowledge for study outcomes, for preparing and promoting treatment, and for general patient management. ML is also used for data analysis, such as data regularity detection by properly handling imperfect data, continuous data interpretation in the intensive care unit, and intelligent alarming resulting in accurate and efficient monitoring
The main interest in medical diagnosis is to determine the presence of a disease followed by a specific description of it. In each disease under consideration there is a different category, and one category in cases where there is no disease. Here machine learning by analyzing patient data increases the accuracy of the medical diagnosis.

The measures in this Machine Learning framework are usually the outcomes of other medical tests ( e.g. blood pressure, temperature and other blood tests) or medical conditions ( e.g. medical images), the presence / absence / intensity of different symptoms, and basic physical details about the patient (age , sex, weight, etc.).

4. Social Media Platform

Social networking is used to provide better news feed and ads, while the interest of the user is achieved primarily through the use of machine learning only. There are several sources on YouTube including recommendations for friends, Facebook page recommendations, albums, and suggestions for videos. This operates primarily on the straightforward principle based on the experience of the user, in which they get linked and quite often visit the profiles or websites, recommendations are being given to the user accordingly. This also includes the technique for extracting valuable data from photographs and videos.

5. Predictions

It helps in developing the applications that forecast the cab or travel price for a given time and traffic congestion where it can be found. While booking the cab and the app, it estimates the approximate price of the trip which is only done through the use of machine learning. When we use GPS service to check the route from source to destination, the app will show us the different ways to go and check the traffic at that time for the smaller number of vehicles and where the traffic congestion is more done or retrieved by the application of machine learning.

6. Spam & Malware

Email clients use a variety of spam filtering and these spam filters are constantly being modified, often by machine learning applications. Rule-based, multi-layer, and tree induction are some of the techniques that machine learning offers. Likewise, a range of malware were found, and these are mainly found by system security programs, which are mostly only supported by machine learning.

7. Search Engine

There are search engines used to provide the best results for customers when searching. There are several machine learning algorithms generated to search the user’s specific query, such as for google. How the website users often open for a single subject that will stay at the top of the page for a long time to come.

8. Customer Service

Many reputable businesses, or several websites, provide the opportunity to chat with a representative of customer service. So, after the customer asks for any question, it is not necessary that the response is provided by the person only, often the answers are given by the chatbot, which extracts the details from the website and provides the response to the customers. Today they ‘re stronger and understand the questions faster and faster, and they still produce a good result by providing correct answers, and it’s achieved only by the use of machine learning.

9. Fraud Detection

It is being used by the companies to keep track of money laundering like Paypal. It uses the set of tools to help them to check or compare the millions of transactions and make secure transactions.

10. Apps

Most apps and businesses use machine learning to conduct their everyday process because it is more reliable and efficient than manual interventions. Netflix, twitter, google maps, Gmail , Google search etc.

Applications of Machine-learning are:

Business-Based Applications

Let’s identify the uses of machine learning based on business line

1.   Manufacturing

As an Industry Manufacturing is the backbone of any healthy economy. Machine learning helps turn the manufacturing sector from automated resource planning to cut time to market.

2.  Marketing

Machine learning plays a key role in personalized digital marketing in a world of 25 billion plus connected devices. Ads clicking prediction, displaying relevant Ads to consumers, recognizing target customers, measuring churns, etc. are important applications of marketing machine learning.

3. Healthcare

Healthcare is possibly the field, where the artificial intelligence would have a remarkable effect. Healthcare is, as a sector traditionally, highly dependent on manual involvement and highly qualified professionals. Yet in today’s world machine learning allows us to make data-driven decisions that can avoid illness, help better treatment of patients, quicker identification of root causes, etc. Google, Facebook, Qualcomm etc., technology companies are spending billions in ML-based healthcare research

4. E-commerce

Advances in machine learning are also important players in the development of e-commerce today. When we visit an e-commerce website, we can see custom suggestions that are accomplished by content-based or collaborative filtering. The availability of large scale consumer data is possibly what holds e-commerce giants ahead of retailers in the market. Machine learning is also used in the design of apparel. Indian e-commerce giant Myntra has several brands built by deep learning systems.

5. Digital Media and Entertainment

Machine learning has significant implications in the fields of digital media , social media and entertainment. Many of the most relevant machine learning applications are customized recommendations (i.e. Youtube video recommendations), user behavior analysis, spam filtering, social media analysis, and monitoring..

6. Energy

Energy is one of the main industries where approaches to machine learning carry tremendous differences. Prediction of power usage and specifications, dynamic cost maintenance per device, lifetime analysis of the hardware are part of machine learning applications in this field. This is also used for the production of renewable energy resource.

7. Banking & Finance

In a digital world, machine learning allows banks and other financial institutions to protect themselves from fraud , money laundering, illicit financial detection, important customer recognition, etc. It also assists financial organizations with stock market forecasts, demand forecasting, delivering tailored banking solutions, etc.

8. Automobile

An automobile is another field in which the machine learning effect is enormous. Nearly every manufacturer of cars uses artificial intelligence to automate fuel consumption, anticipate breakdowns and even self driving. Tesla, Nvidia and so on are betting heavily in self-driving vehicles.

9. Governance and Compliance

Machine learning reshapes the existing systems of government and defence. Security services are now equipped with real-time image recognition, drone surveillance, automated social network tracking, etc., using state of the art deep learning algorithms and infrastructures.

10. Transportation

While using app cab rides, at some point in time you must have observed the dynamic pricing and surge charges. This is also an application of machine learning. User data is also being used to predict the shortest path.

11. Consumer Service

Quick every company uses customer service chatbots. Chatbots are cost-effective, and radically shift the world of customer service. Automated translation and state-of-the-art text to voice and voice to speech systems help resolve the language barrier.

12. Insurance

As business insurance sits on a gold mine of data which is traditionally used only at the level of implementation. Insurers are now provided with useful insights from the data they collect with the aid of artificial intelligence and machine learning. Machine learning is being used for quicker recovery of claims, fraud detection, renewal prediction, churn analysis, etc. It has the ability to be used at any point of the policy life cycle from the new company today two transactions.

13. Human Resource Management

Although it’s early in life, now machine learning is also being used to manage human resources. Organizations such as Amazon, HDFC bank, etc., use bots and video analytics at various stages of their recruitment process. IBM Watson is also used for the optimization of human capital.


Machine learning helps a lot to work in your day to day life as it makes the work easier and accessible. Machine learning and artificial intelligence are no longer part of Hollywood movies or science fiction, it’s implementations are everywhere in our daily lives. That advancement has a positive and negative aspect, and machine learning is not an exception either. While we addressed mainly the positive applications of machine learning in this article, it can also be used as bad. Deep learning systems such as Deep Fakes have a significant effect on human life and privacy. The need for good data governance is also emerging as a need as an growing area of study and applications


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