Top 15 Machine Learning Examples & Applications

With the growing popularity of the term “machine learning” in popular discourses, the technologies applications and examples of use cases have also become a topic of much interest, especially to AI enthusiasts and computer scientists. While a person acquainted with ML in a limited capacity may envision it to be an intangible, complex and highly advanced technology with complex applications, the uses of machine learning in our day-to-day life are much more widely spanned than is realised. 

In this blog, we take a look into the diverse myriad of industries and day-to-day domains in which machine learning models and technologies contribute to the strengthening of the technological service/application. Continue reading to explore more about some of the important examples of ML in everyday life as well as critical industries. 

What is Machine Learning & How Does It Work?

Machine learning is a pillar of the domain of artificial intelligence, and its primary function revolves around the development and training of models guided by algorithms and statistical domains to make accurate predictions and improve decision-making by the model. Machine learning is concerned with the training of models and AI programs to make the most efficient and accurate decisions and solutions based on data analytics of vast datasets. Thus, machine learning technologies involve the suggestions and decision-making based on insights gained from fed datasets rather than direct instructions of performance.

ML models and technologies work by putting statistical techniques to use in the analysis of vast datasets. The ML model then identifies trends, patterns and analytical insights in the data, which are used to predict and guide decision-making and problem-solving in softwares and applications. Over time, with new data being generated through various users of the platform, the ML model continues further autonomous learning and becomes increasingly accurate in the predictions made. 

Thus, the field of machine learning is closely tied to AI as well as data science and analytics, and provides a strong impetus for the use of data-driven mechanisms in computer science for strengthening technological services. 

Top 10 Examples of Machine Learning in Everyday Life

While many users of machine learning and technology enthusiasts may acknowledge the various domains where it finds its usages, the pervasiveness of ML technologies and their services in the day to day lives of people are underappreciated. 

Source: https://financesonline.com/machine-learning-statistics/

Here we have elaborated upon a few of the prominent domains of everyday functioning wherein machine learning makes a significant contribution. 

1) ML in Customer Services

Machine learning technologies are being deployed in customer services through artificial intelligence tools and softwares. These softwares and customer service chatbots are strengthened by integrated ML features. Machine learning has important implications for natural language processing, which is utilised in automated customer service bots. Speech recognition technologies equipped with ML models are also often utilised to understand user and customer intent and accordingly provide solutions to their problems based on such analysis. 

Artificially intelligent virtual assistants used for addressing customer queries in real time also have integrated machine learning models to strengthen the technological foundation of the assistant. 

2) ML in Personalised Suggestions for Users

One of the main purposes of utilising a machine learning model in a software/application is to be able to derive meaningful data insights from available user information in datasets and further use them to provide personalised suggestions and solutions for each user. With the use of statistical techniques and models, ML makes predictions about user preferences, choices and desirable solutions on the basis of their past data. Hence, a number of applications including video streaming platforms, music platforms, lifestyle applications, social media etc. make use of ML to provide the most relevant solutions and personalised suggestions to the users. 

3) ML in Marketing & Sales

Marketing and sales, especially in the digital and virtual domains rely on machine learning technologies to derive analytical insights about consumer data, provide personalised solutions and product suggestions to users, present relevant ads suited to customer needs, generate leads, identify dormant leads, monitor marketing campaigns, their reach and success and so on. 

Source: financesonline.com & explodingtopics.com

Thus, the applications of ML in marketing and sales departments are currently on the rise with a large number of enterprises identifying the potential of machine learning in being utilised in data analytics and automating the processes surrounding effective marketing.  

4) ML in Virtual Assistants

Speech recognition, natural language processing, provision of data-backed solutions, predictions of user preferences based on analytics on datasets, translation from text to speech (or vice versa) etc. are a few of the ways in which ML technologies are integrated into virtual assistants. 

Source: https://financesonline.com/machine-learning-statistics/

Virtual assistants like Siri, Alexa etc. as well as assistant chatbots like Chat GPT, Bard etc. all make use of ML to perform the various functions including speech recognition for inputs, providing accurate results, natural language processing etc. 

5) ML in e-Mail Filtering & Spam Detection

Machine learning models also find significant usage in e-mail applications, especially with respect to email filtering and categorisation into various folders including primary inbox, social emails, spam mails etc. Advanced email filtering features also incorporate ML technologies to identify the various attachments in emails, purpose of emails etc. to segregate them smartly into categories created by the account user based on their customised needs. 

Another major application of machine learning in email filtering pertains to spam detection and spam filtering. With huge datasets to train upon, ML models learn to identify spam emails based on their sources, frequency of mails, purpose of mails and so on.

6) ML in Transportation

In addition to the crucial contributions of ML in self-driving cars, ML models are also actively utilised in a number of other ways in transportation. Some of the most prominent examples of machine learning in use in the transportation industry include the real-time analysis of traffic and possible routes to avoid it (as displayed in location-based applications like Google Maps), matching user preferences (price, route, number of co-passengers, preferred vehicle etc.) to the services provided by transport and ride applications, estimating time needed to reach certain destinations, providing suggestions to users based on their location etc.  

7) ML in Prediction & Recommendation Applications

In addition to providing personalised suggestions based on user data, a large number of applications also put machine learning to use for making predictions related to certain outcomes, such as the weather, stock prices, loan approvals etc. Moreover, based on such predictions, such applications also provide recommendations to users. Both these features rely heavily on the data insights derived from extensive datasets about certain products, environmental conditions (e.g. weather prediction applications), user information (e.g. stock price prediction applications) etc. 

8) ML in Language Translation

Language translation in real-time, translation in the form of text to speech, speech to text, sound to text etc. are some of the primary areas in which machine learning technologies are utilised. In the case of such translatory functions, ML models are trained to provide not only the literal translations from one language to another, but also to conserve the contextual and meaning of the text being translated. Applications of ML in translation are seen in search engine translators, translators in websites, translation applications, multilingual models, language model integration etc. 

9) ML and Automation of Professional Operations

Another major application or use case of machine learning is its role in the effective automation of a number of professional operations and functions. This is especially true in the case of repetitive tasks involving segregation, categorisation, screening based on some criteria and so on. ML technologies are also highly useful in data analytics and identification of data patterns that may be tough to decipher by humans. The automation of such functions enables human resources to devote significant attention to strategic functions in organisations, especially in domains like HRM, data science, business development etc.

10) ML and Sentiment Analysis

Sentiment analysis refers to the ability of a software program to gauge the emotional and sentimental undertones in the inputs provided to them, which can be in the form of datasets, speech inputs, textual inputs etc. Sentiment analysis includes the overall judgement of the sentiment expressed by users in various forms, including customer feedback, product/service reviews, comments in social media etc. This feature is put to use for purposes of marketing, upholding community guidelines (e.g. elimination of aggressive or threatening messages in social media), emotion recognition for research purposes etc.

So, as can be seen, there are diverse applications of machine learning in our day-to-day lives, many of which are lesser known as being enabled and strengthened by machine learning. With the rise of AI softwares and their integration into industries and personal lives of users of technology, the scope and utility of machine learning has also vastly grown. 

5 Crucial Applications and Uses of Machine Learning

In addition to being central to efficient functioning of day-to-day technologies and applications, the rolenor machine learning models and their transformative power has also been identified in certain crucial domains with high stakes. 

Source: https://financesonline.com/machine-learning-statistics/

Elaborated here are a few instances of the significant and indispensable contributions that ML makes in some of the crucial industries. 

1) ML in Finance & BFSI Industry

The applications of machine learning in the finance and BFSI sector is highly crucial for detection of potential financial frauds and curbing them. Based on vast datasets about financial records and transactions, ML models are able to identify trends and insights about account holders’ financial patterns and transactions. With such insights, it is equipped to quickly identify any unusual data patterns, interactions suggesting potential fraud as well as protect financial databases and information from hackers. This is one of the crucial domains in which the contributions of ML technology enables heightened protection of financial resources and data of users. 

2) ML in Cybersecurity and  Fraud Detection Services

As has been outlined in the uses of ML in financial and BFSI sectors, it finds applications in security and vigilance. In addition to spotting potential frauds and safeguarding users and enterprises from them, ML is also highly instrumental in the cybersecurity domain. Common uses of machine learning algorithms in cybersecurity include those like endpoint security, malware detection, anomaly detection, producing automated threat responses (ATRs) etc. 

3) ML in Surveillance and Recognition

Since ML algorithms are integrated into facial recognition systems, speech recognition systems etc. They are also put to use in biometric surveillance, security recognition and related services and applications. The role of ML in surveillance and recognition is put to use in the military and defence sectors, for fraud detection, biometric vigilance measures, for predictive maintenance of existing vigilance systems and so on. 

4) ML in Healthcare

The healthcare industry, especially domains of health maintenance, diagnosis and biofeedback are making significant use of machine learning algorithms and models. For the purpose of maintaining health and treatment planning, ML-powered smart applications are available which provide regular feedback and reminders to patients about their health. 

In the case of medical imaging and diagnosis, many advanced medical softwares have integrated ML technologies to strengthen their diagnostic accuracy. Furthermore, health risk predictive applications of machine learning are also significant for the healthcare and medical industry. 

5 ) ML in Predictive Analytics 

Through the various applications and examples of machine learning discussed in so far, the strength and powers of ML in prediction is indisputable. The use of machine learning for predictive analytical cases are common in risk predictions in healthcare, stock and shares price applications, for business growth and strategization and so on. Thus, predictive analytics are an overarching essential example of machine learning and its power for transforming the life of individuals and functioning of organisations.

Conclusion 

As has been delineated in this blog, the uses and applications of machine learning technologies are not only diverse and widespread but also crucial in certain industries such as healthcare, financial and BFSI sectors, cybersecurity, vigilance etc. The contributions being made by ML in strengthening AI models and services promise bright prospects for the future.

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