Exploring 11 Machine Learning Use Cases with Real-World Examples

In recent years, the machine learning use cases have increased across industries. They are transforming the way businesses operate and innovate. From automating tasks to improving decision-making, ML use cases are playing a vital role in solving complex challenges. 

The artificial intelligence (AI) revolution is here, and it’s time we grab the opportunity and make the most of it. One of the most fascinating aspects of AI is machine learning (ML). Think of it as a branch of AI along with computer science where engineers help AI to imitate the way a human would think, respond, and create conversations. 

With so many machine learning use cases, it has almost become an inevitable part of the tech industry. Today, machine learning as a field has become a lucrative job prospect for young professionals who are looking to rise up the ladder. It is quite evident that AI is now a permanent part of our lives, and if you are an AI enthusiast, this is your call to conquer machine learning!

In this article, we explain machine learning use cases with examples to help your understanding of the domain. 

Also read: What is Machine Learning? A Comprehensive Guide

11 Machine Learning Use Cases and Examples You Need to Know

Recent years has shown an increasing proliferation of machine learning use cases. You probably think that a person who has nothing to do with the tech industry is far off understanding machine learning. However, the reality stands quite different. 

You have probably had some encounters with ML capabilities in the form of these use cases. All of these examples prove why learning ML is worth the hype and grind. Let us go through these use cases and examples one by one:

1. Machine Learning Use Cases in AI Chatbots

None of us would be surprised to see chatbots making it first on the list of machine learning use cases. All of us have had some kind of interaction with virtual chatbots in one way or the other. As time has progressed, chatbots have only become more powerful with each passing day. Aptly mimicking a human conversation, they can answer anything, no matter the question. 

However, a lot of work goes into building these chatbots, and machine learning is one crucial aspect of it. ML, along with natural language processing, trains the chatbot model to work on an extensive data set and enables it to interact and engage the end user.

The other thing about machine learning capabilities in chatbots is that they produce more accurate responses and answers. You would also have noticed that the conversations feel as if you are talking to a human. All of these have been made possible with the help of machine learning.

There are various machine learning examples of chatbots that are taking all over the world. From Siri to ChatGPT, one can say with surety that there is no dearth of powerful chatbots.

2. Machine Learning Use Cases in Product Recommendation

If you are someone who likes to actively shop online, you must have encountered this scenario. Many times, we see product recommendations on different pages and websites based on our preferences and likings. 

This machine learning use cases has been made possible by machine learning and is responsible for changing the world of the retail and streaming industry. The way this works is that ML engineers train the model to pick up the user’s history of purchases. 

It also makes use of analytics such as the customer’s age, gender, and other characteristics. It then tracks the available inventory and recommends products that are suitable for you and, very accurately, to your liking. This has boosted the sales of product-based industries and has been a game-changer in every aspect, be it retaining customers or encouraging them to just click on it once!

A lot of famous companies use ML-powered recommendation engines on a daily basis. Let’s take for instance fashion-based apps like H&M or Zara. Once you open them, you will see options that have been customized to suit you the best. From the color to the design, everything is exclusively recommended to you.

3. Machine Learning Use Cases in Facial or Biometric Identification

A decade back, it was almost impossible to imagine a scenario in which you could have marked your attendance through facial biometrics. It is not just for attendance purposes; it is also one of the best ways to identify and track people who are engaged in criminal or other illegal activities. 

But how does this work exactly, and what’s the science behind it? Artificial intelligence and machine learning are again at the forefront of this technology! Facial recognition functions by identifying data patterns. 

It particularly uses a subset of ML called deep learning, which makes use of neural networks to teach the model to process data in a way that is just like a human would. ML engineers also apply special algorithms that search for particular facial zones like the nostrils, human eyes, iris, and mouth to detect the person. 

This is being further developed to the point where models can predict a person’s emotions based on their eye movements or other facial gestures.

To talk about machine learning use cases, facial recognition is being actively used by Facebook to recommend tags. A lot of surveillance cameras deployed in investigation units also use this technology to track criminals or lost individuals.

Also read: Machine Learning vs. Data Science — Which Has a Better Future?

4. Machine Learning Use Cases in Spam Filtering

As digital citizens, we really can’t function without our emails. Needed for both professional and personal instances, email has existed for quite some time now. However, it would be really frustrating to see unnecessary and unwanted messages taking a huge chunk of your inbox. For this, software engineers have again used machine learning to filter messages based on their content. 

Like, there are dedicated spam boxes where unwanted messages go. Then, there are separate boxes for promotions and social media. Thanks to machine learning, our email boxes don’t look as cluttered as they used to many years back. This again works by identifying patterns and keywords within the content. 

ML models have already been trained with a huge data set to identify various types of messages. Now, once there is an incoming mail, the model reads and segregates it based on data patterns.

A prime example of machine learning use cases is Gmail’s inbox layout. If you notice, you have a spam box, separate updates, and promotions box too apart from your primary inbox. This has been effective for a long now and has made our mailing experience hassle-free and decluttered.

5. Machine Learning Use Cases in Educational Solutions

It is widely agreed upon that each student has a different method of learning. All of us process information and store it in ways unique to ourselves. Some kids may also learn better from visual aids rather than textbooks. 

Today, online education has also become a common practice in most households, and the most exciting part of this is that online learning models are leveraging machine learning to tailor the education content to the student’s behavior, interests, and learning habits. 

This is perhaps one of the most apt machine learning examples. ML engineers have successfully mastered this technique, where the model would read into and gauge the student’s level of understanding and learning concepts. 

They would then utilize other criteria, as mentioned, to create educational material that students would understand the best. Moreover, the teaching process would also vary from one kid to another.

6. Machine Learning Use Cases in Social Media Optimization

To speak metaphorically, netizens live, eat, and sleep on social media today. Similarly, social media platforms are also thriving because of their wide and extensive base of users. 

To retain these users, they have to thus become more efficient in handling social media content and providing more innovative ways to engage them. For this, machine learning has again been of help. 

With ML algorithms and neural network processing, social media platforms would show you content that is suited to your liking. Let’s say you like searching for vacation pictures; then the platform will show you a vacation picture every time you open up the platform. Moreover, machine learning has also been useful in detecting cyberbullying and ensuring safety in cyberspace. They can effectively flag content that is inappropriate just by identifying patterns within them.

There are various machine learning use cases that we can talk about when it comes to machine learning algorithms in social media. 

For instance, there is Instagram. It very well tailors and displays your content as per your preferences. YouTube is also another example that recommends you videos on your past watch history. All of these platforms can also detect spam or bullying content and caution you before you proceed.

Also read: 12 Machine Learning and Artificial Intelligence Jobs

7. Machine Learning Use Cases Sentiment Analysis

This is another one among many exciting machine learning use cases that have again been made possible by technology. So, what happens is that models can detect your sentiments and emotions through the text you input. 

Suppose you type a sentence, and the machine learning model can predict your current mood. They will analyze the language, such as your tone, and then categorize it into sections, such as happy, sad, or neutral. 

Let’s say, a business gets thousands of feedback each day. Now, it is impossible to manually go through each one. However, as a business official, you would also want to gain insights from it. ML models will automatically detect the sentiments in each review and then categorize them as good, bad, and neutral based on the tone used.

A lot of companies are using examples of machine learning in feedback collection and review. For instance, there are platforms like Walmart or Amazon that use ML algorithms to segregate feedback.

8. Machine Learning Use Cases in Optimizing Supply Chain Management

Large-scale manufacturing or shipping units also utilize machine learning to power their operations. Firstly, it is very useful in inventory management and can automatically predict and alert when a supply is low in stock or needs to be stocked urgently. Secondly, it has made logistics and supply chain a lot easier. 

As for machine learning use cases, ML models can effectively track the supply chain pathway and alert authorities when anything diverts from the normal line of work. It can also analyze parameters like weather and road conditions and give caution in case there are chances of accidents. 

So, ML has also been mitigating risk levels and ensuring that the supply chain pathway remains error-free.

A lot of shipping companies like FedEx or other courier services have successfully integrated ML models into their line of work. It keeps them up to date with the latest technology and also reduces their margin error to a bare minimum. Overall, operations have benefited a lot from such examples of machine learning.

9. Machine Learning Use Cases in Healthcare Innovations

Healthcare is one aspect of our lives that we can’t compromise on. As a result, scientists, along with engineers, have been constantly looking for ways to develop new ways to enhance healthcare. Machine learning’s integration in healthcare is one such scenario that has elevated medical technology by leaps and bounds. 

Today, machines and IoT medical devices can efficiently predict a patient’s condition, the best solution to their condition, and the best path to preventative care. They have progressed to the point where they can even read scanned images and assist in diagnostic services.

An example of machine learning use cases in healthcare would be Path AI, which provides you with pathology assessment features. It has indeed made diagnostic and medical intervention a lot more accessible and easier than before.

10. Machine Learning Use Cases Dynamic Pricing

Since machine learning utilizes a user’s search history, it can also effectively offer dynamic pricing based on recent trends and the user’s affordability. We often see a product being recommended at different prices to two individuals.

Here the model looks at and analyses the buying pattern of customers and then offers a price that is based on that particular day’s stock market scenario and even the person’s latest purchase amount. So, if you are someone who buys high-end products, the engine would recommend more luxury products.

There are lots of instances that we can take in this scenario. For machine learning use cases, let’s take Amazon. You may see a product priced at a certain amount today, which may be different from the previous day. This is how dynamic pricing works.

Also read: Fraud Detection: How ML Safeguards Against Financial Crimes

11. Machine Learning Use Cases in Employee Onboarding

HR teams have also benefited from machine learning algorithms. Today, it has become a lot easier to board employees with ML-powered interviews and selection processes. With effective ML algorithms, it has become quite easier to identify suitable candidates based on the requirements of the company and their unique needs. 

Moreover, even in the onboarding process, ML-powered algorithms are making it easier for HR professionals to provide customized orientations based on the joint. This includes assessing the joiner’s understanding and problem-solving skills.

For example, platforms like Monday and Slack use machine learning to provide new entries and other staff with their specific task allotments for the day. Such machine learning use cases have optimized the tech industry as a whole.

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FAQs: Machine Learning Use Cases

Q1. How Long Does It Take To Learn Machine Learning?

Initially, it will take some time to learn. However, with the right approach, you can master it in no time.

Q2. How Much Can I Expect To Earn If I Choose Ml?

A lot of ML engineers are earning high salaries. It is currently one of the most in-demand jobs and a high-paying one.

Q3. What Are Some Subsets Of Machine Learning That I Can Learn?

There are many machine learning subsets. For example, deep learning and neural processing focus on individual aspects of machine learning.

Q4. Do Machine Learning Engineers Have A Difficult Work-Life Balance?

No! While machine learning jobs can be demanding, they are also quite exciting. You will be trained to use certain tools and datasets. Once you have mastered them, you will have no difficulty. You can very easily manage your work-life balance after that.

Q5. How Do I Become A Machine Learning Engineer?

You can become an ML engineer if you have a background in software engineering. You can then take specialized courses in machine learning.

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