HomeTechTransforming Industries: Machine Learning Use Cases

Transforming Industries: Machine Learning Use Cases

From chatbots to robots performing tasks, machine learning is to be found everywhere. Despite the vast application of ML and demand for engineers, there is still a shortage of ML engineers, which is due to huge demand. To give you more insight of what machine learning can do, we are going to discuss applications of ML. Through these you can understand where it can be applied. 

What is Machine Learning?

ML is a disciplinary field which lies under the umbrella term AI. It allows machines to learn and improve themselves, or the result they give, based on provided data, a.k.a. training data. They keep on learning from the experiences while accurately providing the results we need. ML needs very less human interaction with the machine so it is called self-learning. 

ML, as a solution, is applied to problems where application of algorithms is not fitting or solving the problem well enough. 

Common Applications of Machine Learning

Now that your concepts of machine learning are clear, and you are ready to hire machine learning developers. Let’s have a look at what type of work can be done using machine learning. Remember this is a small list, machine learning is capable of much more. 

Image Recognition

Imagine recognition is one of the most in demand and highly applied applications of machine learning. Image recognition is exactly what it spells, recognizing images to detect patterns, objects, or other types of data you want. The technique uses analysis of images and finds for data like faces, animals, buildings etc, and patterns. An example of this we can see in Google photos, automatically recognizing the person or scenery from an image and making a group.

Speech Recognition

ML developers can measure words, the intensity they are spoken at, and speech signals. This is also a field that we use almost everyday, and something that has been developed very fast. A common example of this is: smartphone voice assistants and home assistant. 

Traffic Prediction and Patterns

Have you ever been stuck in traffic and thought, I wish I could have known this route was jammed. Well, it is possible, geolocation service applications like Google maps can reroute the path and give suggestions. It can see if the route you are taking is or will be having high traffic flow and suggest you the fastest and shortest route to the destination. 

Product Recommendations

Product recommendation is the application of ML engineering where you see suggestions to buy products. Usually, the suggestion is made based on two patterns. First, if you buy eggs, jam, and butter ML model will suggest you to buy bread because these things go together. Similarly, these products will be assorted in one section too. Second, the pattern is to recommend a product that other customers buy with the product you are buying. 

It also remembers customers buying habits, browsing habits, and carts to give you best suggestions. 

Self-Driving Cars

What is the first thing that comes to mind when you think of self-driving cars? Self-driving cars are unsupervised ML developer work. This enables a machine combined with a computer, i.e. the car, to think for itself and act based on the data, which is collected through cameras and sensors, and processed in the computer. 

Email Spamming

Identifying spam emails is another application of machine learning, it is widely used professionally, however not everyone knows about its application. Email services providers today have built spam detection systems into their software which classifies and separates billions of emails daily.

Malware detection

In today’s computer systems, ML engineering has been embedded to detect malware. If the user allows further processing, it can either delete or archive that malware. Usually, it requires permission for further action. You can hire machine learning engineers to get malware detection for your software and business applications. 

Online Fraud Recognition

Similarly, businesses have associated ML models with their software that detects fraud. For instance, when a customer tries to perform a transaction the ML model examines their activity along with other information and recognizes a pattern and prevents it if it thinks a fraud is about to happen.


Trading is another aspect where recently ML expertise has been applied. Through algorithmic calculations required data is extracted which can then be used to automate or support your investment portfolio. 

You can get suggestions or even let the action be completed by the ML model for buying, selling, and holding the stocks. 


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