What are Machine Learning Applications? Top 10 Industry and Real-World Use Cases

From voice assistants to self-driving cars — Machine Learning (ML) is everywhere! However, in spite of its wide range of applications, advancements in this field are being stalled due to a shortage of skilled employees. According to a survey by Statista, 82% of enterprises worldwide have a demand for machine learning skills, while only 12% of them admit that the supply of these skills is at an adequate level. To bridge the current talent gap, we will discuss, ‘what are machine learning applications’. This should enable aspiring professionals to acquire the required skill sets and excel in this field. 

What is Machine Learning?

ML is a discipline of Artificial Intelligence (AI) that provides machines with the capacity to automatically learn from data and previous experiences by identifying patterns to generate predictions for new processes with minimal human intervention. Machine learning comes to the rescue in several situations where it is impossible to apply strict algorithms.

What are the Common Applications of Machine Learning?

Let’s explore some prevalent machine learning applications across various industries. 

1. Image Recognition
   

   One of the most notable machine learning applications is image recognition, which is a method for cataloging and detecting an object or feature in a digital image. In addition, this technique is used for further analysis, such as pattern recognition, face detection, and face recognition. 

2. Speech Recognition
   

   ML software can make measurements of words spoken using a collection of numbers that represent the speech signal. Popular applications that employ speech recognition include Amazon’s Alexa, Apple’s Siri, and Google Maps. 

3. Predict Traffic Patterns
   

   To explain this, let’s consider the example of Google maps. When we enter our location on the map, the application collects massive amounts of data about the present traffic to generate predictions regarding the upcoming traffic and identify the fastest route to our destination.   

4. E-commerce Product Recommendations
   

   One of the prominent elements of typically any e-commerce website is product recommendation, which involves the sophisticated use of machine learning algorithms. Websites track customer behavior based on past purchases, browsing habits, and cart history and then recommend products using machine learning and AI.

5. Self-Driving Cars
   

   Self-driving cars use an unsupervised learning algorithm that heavily relies on machine learning techniques. This algorithm enables the vehicle to collect information from cameras and sensors about its surroundings, understand it, and choose what actions to perform.

6. Catching Email Spam
   

   One of the most popular applications of machine learning that everyone is familiar with is in detecting email spam. Email service providers build applications with spam filters that use an ML algorithm to classify an incoming email as spam and direct it to the spam folder. 

7. Catching Malware
   

   The process of using machine learning (ML) to detect malware consists of two basic stages. First, analyzing suspicious activities in an Android environment to generate a suitable collection of features; second, training the system to use the machine and deep learning (DL) techniques on the generated features to detect future cyberattacks in such environments. 

8. Virtual Personal Assistant
   

   Virtual personal assistants help people access relevant information via text or voice. When a query is put into the system, the personal assistant gathers information by searching for it or recalling similar questions an individual has asked in the past. Some popular ML techniques involved in virtual assistants include speech recognition, speech-to-text conversion, natural language processing, and text-to-speech conversion. 

9. Online Fraud Recognition
   

   One of the most essential applications of machine learning is fraud detection. Every time a customer completes a transaction, the machine learning model carefully examines their profile in search of any unusual patterns to detect online fraud.

10. Stock Market and Day Trading
    

    When it comes to the stock market and day trading, machine learning employs algorithmic trading to extract important data to automate or support crucial investment activities. Successful portfolio management, and choosing when to buy and sell stocks are some tasks accomplished using ML.  

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Best Machine Learning Applications and Examples

With an understanding of the common machine learning uses, let’s explore some examples of the popular applications in the market that rely heavily on machine learning.

1. Social Media (Facebook)

Automatic friend tagging suggestions on Facebook are one of the best machine-learning applications. Facebook automatically locates a face that matches its database using face detection and image recognition and then advises us to tag that individual using DeepFace (a project of Facebook’s Deep Learning division).

2. Transportation (Uber)

Uber is a customized cab application that relies on machine learning to automatically locate a rider, and offer options to travel home, to work, or to any other regular location based on the rider’s history and patterns. Moreover, the app further uses ML algorithms to make precision predictions around the Estimated Time of Arrival (ETA) to a particular destination by analyzing traffic conditions. 

3. Language Translation (Google Translate)

To break all language barriers and make traveling to foreign countries easy, Google Translate employs Google Neural Machine Translation (GNMT) which relies on Natural Language Processing(NLP) to translate words across thousands of languages and dictionaries. It also makes use of POS Tagging, Named Entity Recognition (NER), and Chunking to maintain the words’ tonality. 

What are Machine Learning Applications in Healthcare?

To understand what are machine learning applications in healthcare, let’s explore the following uses and examples. 

1. Personalized Treatment/Medication

Machine learning is crucial in identifying the types of genetic markers and genes that respond to a specific treatment or drug. By allowing improved disease assessment, machine learning combines advanced analytics to make personalized medication or treatment based on individual health information, a reality. This helps to lower overall healthcare expenses.

2. Genetics and Genomics

Machine learning in genomics influences multiple areas, including genetic research, gene modification, and genome sequencing, to identify the underlying impact of heredity on human health. Additionally, another emerging application involves predictive testing for early disease diagnosis to improve the quality of patient care. 

3. Healthcare for Personalized Treatment

A common and serious issue for the healthcare industry is when a possible medication only benefits a small portion of patients and has significant side effects in others. To resolve this problem, Genentech collaborated with GNS Healthcare to apply innovative solutions using biomedical data. Genentech used AI and ML for GNS Reverse Engineering and Forward Simulation technology to identify patient response markers based on genes that could lead to targeted therapies. 

4. Cancer Prognosis and Prediction

As ML algorithms can identify critical traits in complicated datasets, it is applied in cancer research. It is used to construct prediction models using techniques like Artificial Neural Networks (ANNs), Bayesian Networks (BNs), and Decision Trees (DTs). This helps in precise decision-making and modeling of the evolution and therapy of malignant diseases. 

5. Drug Discovery/Manufacturing

To manufacture or discover a new medicine, millions of compounds need to go through a series of tests, which is expensive and time-consuming. This lengthy multistep drug discovery process can be sped up using ML.

6. Drug Discovery

One of IBM Watson’s most important applications is for drug development. Pfizer leverages IBM Watson’s machine learning capabilities in their immuno-oncology research to filter out data and choose the most suitable participant for a clinical trial, facilitating faster drug discovery. 

What are Machine Learning Applications in Finance?

Wondering what are machine learning applications in finance? Explore the list of machine learning uses in the banking and finance industry for an in-depth understanding. 

1. Focused Account Holder Targeting

How do banks know who are their most valuable customers? The underlying machine learning algorithms in the customer segmentation software help them identify the account holders with sizable balances and loans. This helps banks to better identify customer transaction trends and pinpoint their most influential and preferred banking clients. 

2. Fraud Detection

Fraud prevention is one of the most significant uses of machine learning in the banking and finance industry. This technology is implemented to search through large volumes of transactional data and spot patterns for unusual behavior. Every purchase a customer makes is evaluated in real-time, and the likelihood that the transaction is fraudulent is indicated by a fraud score. The transaction is subsequently blocked or frozen for manual examination in the event of a fraudulent transaction. This entire process takes place in just a few seconds.

3. Loan Eligibility Prediction

The foundation of the banking industry is loans and the interest they produce. Therefore, proper validation is required before selecting who should or shouldn’t be granted a loan. For this purpose, banks implement classification algorithms, such as Logistic regression, Naive Bayes Classifier, and Random Forest to classify their customers as ‘churners’ or ‘non-churners.’ 

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Applications of Machine Learning in Real-World

Machine learning is a modern innovation with many real-world applications—across industries as well as in daily lives. Whether you are going to work (using Google Maps for finding low-traffic routes) or making an online purchase (on Amazon or Walmart) or communicating with your friends online (Meta) — this technology accompanies us every step of the way. It is only a matter of time before we observe more practical machine-learning applications unlocking new technological advancements.  

So if you are interested in mastering the in-demand machine learning skills, then look no further. Explore a wide selection of machine learning and artificial intelligence courses offered by the world’s top universities in association with Emeritus and get hands-on knowledge and experience working on real-world ML projects. Happy Learning!

By Rupam Deb 

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