Top Machine Learning Applications in Mobile Apps


Juned Ghanchi writes about the top machine learning applications in mobile apps, some of which many of us use on a daily basis.

Mobile apps, thanks to their all-pervading and all-encompassing role across all spheres of life, have been the subject of several state-of-the-art technologies and innovations. For mobile apps to stand out from the crowd, new technologies are playing an instrumental role. As the demand for personalised user experience is exponentially growing across all digital applications, new technologies like Machine Learning and Artificial Intelligence are playing a decisive role in meeting this demand. 

But how can Machine Learning empower mobile apps? ML is being used by mobile app developers to provide enhanced features, ranging from face recognition and person detection to personalized recommendation engines. Here are some interesting machine learning applications in mobile apps. 

Finance and Banking

Predictive analysis in the finance and banking industry is of tremendous significance since the precise prediction of crashes, economic bubbles or trends can help organisations stay clear of the risk factors while optimising growth opportunities. Insurance startup Lemonade has launched a smartphone app, which uses ML and chatbots to provide insurance services. 


Healthcare is another crucial sector, where machine learning is expected to play a massive role. From precision-driven diagnosis based upon the user behaviour to making way for more proactive and responsive healthcare based on patient input, this technology can bring a lot of efficiency and reliability to the modern healthcare practices. For certain life-threatening diseases like cancer that demands early detection and diagnosis, proactive learning of the patient’s symptoms can really play a vital role. Machine learning can also pave the way for more personalised medication and treatment for ailments of different nature. Wearables and their associated mobiles app play are currently playing a huge role, helping to monitor health in real-time and provide feedback. 

Retail and ecommerce 

In the whole retail sector, including ecommerce stores, knowing customer behaviour and habits play a crucial role. Knowing customer preferences, leanings and intent can help stores to address customer needs and choices more precisely and in a relevant manner. Personalised recommendations based on user inputs can help a store take on the sales opportunities in a more precise manner. Some of the key areas where ecommerce app developers can really reap the benefit of ML-based insights include product search, recommendations, trend forecasting, promotions, and fraud control mechanism. An example of a mobile shopping app using ML is ecommerce giant Amazon. 

Advertising & Marketing

Several brands are tapping into the power of ML for showcasing relevant ads to targeted users. Coca cola, for example, uses an image recognition algorithm to automatically detect images of its products when users upload photographs on social media. Based on this information, it then taps into the conversation and generate ads to relevant audiences. Some companies are also using geolocation to show you mobile notifications when you’re closer to a store you’ve already looked up products on. 

Read more from Kolabtree: 5 Companies Using Big Data and AI to Improve Performance

Real-World Applications of Machine Learning in Mobile Apps 


Netflix, the streaming video and media app, utilises machine learning to provide enhanced user experience and improve user engagement. Netflix uses ML to cater to the user’s preferences, choices and intent, based on user activities. Netflix research outlines how ML is being used effectively across their network.  


Tinder, the globally popular dating app, has already broken all records in terms of user engagement and satisfaction among all other dating apps. Tinder now uses a Machine Learning algorithm to understand user intent and preferences more precisely, and figure out how to show you a profile you’re likely to right-swipe on. Vox explains the Tinder algorithm here


Machine Learning is not only about offering customers perfect recommendations to ensure a consistent sales output. Snapchat is one of the few successful apps that utilised the full-length capabilities of Machine Learning technology. Filters like 3D Paint in Snapchat are great examples of how augmented reality and ML can be used side together for enhanced computer vision

Google Maps

The use of Machine Learning by Google Maps is another prominent example of how this technology can ensure optimum efficiency and usability for end-users. Instead of waiting every time for the input and command of a user, Google Maps uses ML to predict
bus delays, read street names, and more. 

In conclusion, ML and AI are paving the way for smarter and customer friendly applications that were unthinkable just a couple of years ago. The future of mobile apps and digital interactions belongs to these smart technologies.

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About Author

Ramya Sriram manages digital content and communications at Kolabtree (, the world's largest freelancing platform for scientists. She has over a decade of experience in publishing, advertising and digital content creation.

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