machine learning

Application of Machine Learning in Real Life – Explained with Examples

Summary: Machine Learning revolutionises the healthcare, finance, and social media industries. Its impact on daily life is profound, and its applications range from disease diagnosis and financial automation to personalised recommendations. As its use grows, responsible implementation is vital to ensure its benefits outweigh potential risks.

Introduction

The rise of Machine Learning has significantly impacted how businesses and other organisations operate in the modern world. Machine Learning has made it much easier for organisations to identify patterns and make effective decisions based on that information. 

The following blog explains and provides examples of Machine Learning in real life. It explores how this technology is used in the real world and how it is transforming business.

What Is Machine Learning?

Machine Learning is a field of computer science that allows computers to learn from data. This can be used in various ways, including improving the accuracy of computer programs’ predictions or automatically detecting patterns in data. 

It can also be used to build systems that can learn without human input and carry out tasks that previously required significant computing power.

Machine Learning is widely used in industry and many areas of science and research. It can make a wide range of tasks much more efficient, accurate and less expensive than they would be if carried out by traditional means. 

However, if it is not used correctly, it can also be used to make dangerous decisions and potentially harm people. Because of this, several steps must be taken to ensure that it is being used responsibly and that any potential risks have been addressed.

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Real-World Applications Of Machine Learning

To understand how Machine Learning is applied in the current world, we need to go through the application of Machine Learning in real life, which is explained with examples that the following sections will evaluate. Let’s explore some of the applications of Machine Learning in day-to-day life with examples:

Social Mediamachine learning

Social media is constantly changing and becoming more straightforward to use as new technologies and techniques are introduced. Users are spending more and more time on social media, which, in turn, provides businesses with a wealth of information about consumer preferences that they can use to improve their products and services. 

In recent years, social networks have started to develop Artificial Intelligence (AI) systems capable of recognising patterns and trends. Algorithms have been designed to identify which content is relevant to a specific user based on his or her interests and previous behaviour on the network. 

This allows companies to tailor advertisements to individual users and improve the effectiveness of their marketing campaigns. 

Facebook is an example of a popular AI system used by social media networks. The company has developed its own AI assistant, “M,” which can learn and improve over time as it interacts with its users. 

The application of Machine Learning in day-to-day life as a system is intended to act as a virtual guide that understands what each user is looking for and will provide them with relevant information based on their needs and preferences.

Product Recommendations

Machine Learning can help personalise product recommendations based on the user’s interests or past purchases. This is becoming increasingly popular on eCommerce websites and is often done through a chatbot. 

For example, Amazon uses an AI algorithm called “Amode” to recommend products to customers based on items they have previously purchased on the site and items other customers with similar profiles have purchased. 

This application of Machine Learning in daily life allows users to make informed purchasing decisions by providing recommendations based on their preferences.

Healthcaremachine learning

The rise of big data has led to numerous advancements in healthcare technology in recent years. Doctors are now using advanced digital tools to collect and analyse vast amounts of patient data to improve their quality of care. 

These resources can also be used to detect potential health problems before they arise and identify patients who are at risk so they can be given the necessary treatments. One area where this is particularly effective is in disease management.

Machine Learning algorithms can monitor patients for signs of certain diseases and alert doctors to potential problems so they can be treated as soon as possible. Machine Learning is also enabled within hospitals to predict patients’ waiting times. 

The details regarding the staff, the use of emergency rooms, patients’ records, the layout of the emergency rooms, and the chat logs in every department can be used to define the ML algorithm. 

Machine Learning in Healthcare is a technology that has the potential to revolutionise healthcare delivery and make it safer and more cost-effective in the long run.

Supporting Animal Conservation

The private sector often sets the pace for progress in science and environmental protection. Private donations and investments have helped to accelerate research and development efforts in many areas, including wildlife research. 

Scientific and conservation organisations worldwide have used Machine Learning and Deep Learning to improve the efficiency and accuracy of their research and conservation efforts. Such technologies include image analysis software and satellite monitoring tools that can gather data about animal populations and habitats inaccessible to humans. 

Microsoft Azure Machine Learning can process and analyse vast data from these sensors to provide insights into animal movements and habitat usage patterns that can be shared with the scientific community and the general public.

Must See: How to Become a Certified Microsoft Excel Expert?

Sentiment Analysis

Sentiment analysis is the computational analysis of written text to determine its mood or emotional content. It can be performed manually or by machine. 

Recently, it has become increasingly common for companies to use sentiment analysis programs, which use Machine Learning and Deep Learning to assess public opinion about their brand, products, and services. 

Marketers can use this information to identify positive and negative trends in public perception and trending topics, forming the basis for future marketing campaigns.

Analysts can also identify areas needing improvement to enhance customer satisfaction and reduce customer complaints. However, using sentiment analysis software presents specific challenges.

For instance, Machine Learning can sense the tone or sentiment behind the written text if you’re writing an email or a review. Evaluators can use sentiment analysis to assess the sentiment of a review-based website, decision-making application, etc.

For example, the accuracy of the results often depends on the quality of the data collected and analyzed. To be effective, sentiment analysis programs require large volumes of high-quality data to train the system and identify trends over time. Additionally, the application of Machine Learning and Deep Learning makes it more accessible.

In addition, the results obtained from sentiment analysis programs may be subjective and, therefore, unreliable compared to other types of data analysis. These factors can make the process of using sentiment analysis programs rather complicated and time-consuming.

Financial Services

The widespread adoption of automation technologies, including Machine Learning & Deep Learning applications, is already transforming financial services. Banks and financial institutions use these technologies to automate various business processes. Common examples include underwriting loans, verifying identity documents, processing payments, and responding to customer inquiries. 

These applications of Machine Learning in daily life provide greater efficiency and improved productivity. They do this by eliminating unnecessary manual processes and minimising the margin of error. They also enable banks and financial institutions to serve their customers better and provide higher service levels. 

As these automated processes become more commonplace, they can potentially disrupt the financial services industry as we know it and transform how banks and other financial organisations operate.

Financial institutions have understood the importance of Machine Learning, considering that they have introduced several systems that can efficiently handle paperwork and customer data. 

This may include customer profiling, using data to assess past expenditures, and analysing the probability of risks, fraudulent activities, or failures. Banking products could now be based on customers’ purchasing power, laid out through the data conquered using Machine Learning algorithms

For instance, Deserve, a fintech company that uses AI to help consumers establish credit scores based on their spending patterns, raised 2.025 billion INR in its Series A funding round in March 2018. 

This brings the total capital the company has raised to 3,000-3,200 crore INR since its launch in 2015. The company is now looking to expand its platform and introduce new products that will help people improve their credit ratings.

Improving Language Translationmachine learning

Machine translation is a field of computer science. It involves the development of computer algorithms that allow computer programs to translate text from one language to another. The process of machine translation involves several steps. 

Google Translate utilises deep neural networks that can perform tasks automatically. For example, It translates text from one language to another. It also identifies relevant words and phrases based on the context of the surrounding text. 

You can use the output of these programs to perform similar tasks in the future. Therefore, it eliminates the need for human operators or time-consuming manual editing processes. The ability to translate text automatically has the potential to accelerate the global exchange of information significantly. This information exchange will rapidly happen between people from different languages and cultures.

Enhancing Cloud Services

Cloud service providers such as Amazon and Microsoft offer cloud computing services. These services enable businesses to store their data remotely on servers worldwide. This reduces the cost of maintaining their servers and frees up their resources. Thus allowing them to focus on growing their businesses. 

However, large files are frequently too large to send over the Internet, slowing data transfer and making it unreliable. This has led to the emergence of services such as Amazon S3 and Azure File Storage. These services allow users to store large amounts of data on remote servers without downloading them to their computers. 

These cloud storage services allow thousands of businesses and institutions to upload and download data easily. Users do not have to deal with the inefficiencies of traditional data transfer methods.

Read Further: 

Azure Data Engineer Jobs

Unlocking Deep Learning’s Potential with Multi-Task Learning.

Frequently Asked Questions

What Is The Term For Applying Machine Learning To Large Datasets?

Machine Learning algorithms imply applying Machine Learning to vast datasets. These algorithms enable computers to learn patterns and make predictions autonomously. Thus making them essential for processing and analysing large volumes of data efficiently and accurately.

How Does Machine Learning Impact Healthcare?

Machine Learning algorithms monitor patient data to detect diseases early, predict hospital waiting times, and even assist in treatment decisions. By analysing vast amounts of medical data, Machine Learning enhances diagnosis accuracy and significantly improves patient care outcomes.

Can You Provide Examples Of Real-World Machine Learning Projects?

Real-world Machine Learning projects encompass diverse applications. For example, sentiment analysis on social media platforms, personalised product recommendations on eCommerce websites, and predictive diagnostics in healthcare. These projects leverage Machine Learning to solve complex problems and enhance user experiences in various domains.

Wrapping Up

Machine Learning applications in industry and their use in real life are increasing daily. It also has actual use cases to solve real-world problems in various domains. 

Apart from being complex, AI can also be beneficial if implemented correctly. The AI market will be worth over 16,60,000 Crore in the next 10 years. AI has immense potential to transform the way we work and the way we live our lives. 

Thus, Machine Learning is here to stay! Machine Learning in Real Life has become an integral part of our daily lives. It will continue to make its presence felt in the future.

Authors

  • Asmita Kar

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    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.

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