How to Use Machine Learning to Build Innovative Mobile Apps

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Developing a machine learning app? Explore this blog about machine learning app development, model training, costs involved, and other technical and business aspects.

Machine learning has been transforming the digital world for quite some time, and there are arrays of businesses it can influence.

Machine Learning is a subset of Artificial Intelligence that helps organizations to understand and anticipate consumer behavior, gain better insights, and offer highly personalized solutions using algorithms that learn from the results and evolve continuously.

Machine Learning offers accurate predictions that can help organizations to take intelligent action without human intervention.

The incorporation of machine learning into mobile applications not only makes them smarter but also enables them to offer unprecedented opportunities to the end-users.

If you are planning to develop a successful mobile app by integrating machine learning, then this article will certainly help you understand the nitty-gritty of its benefits and use cases.

We will explore the types of ML algorithms and use cases where Machine Learning could be used for the development of a domain-specific mobile application.

But first, let us explore why you should use machine learning in your mobile app.

Why should you use Machine Learning to develop a mobile app?

Machine Learning to develop a mobile app

Machine learning provides exceptional abilities to capture and analyze user behavior patterns to offer recommendations and suggestions.

These capabilities can be extensively used in audio and video recognition apps, e-commerce apps, and even in media and entertainment applications.

Your business must develop an ML aided mobile app as :

  • Per BCC Research, the global Machine Learning market is estimated to cross $8.8 billion by 2022.
  • 76% of organizations avail higher sales past ML integration.
  • 50% of organizations are implementing ML learning to transform their sales and marketing functions.
  • ML offers a better capability to predict user behavior and improve business processes.
  • It enables you to explore innovative technologies that can be integrated with ML algorithms.
  • It improves app personalization and searches experience for the user.
  • It supports applications for audio and visual detection functions.
  • It offers advanced data mining capabilities to increase app effectiveness.

Types of Machine Learning algorithms used for Mobile apps

Machine Learning is a technique of decision-making and automated data processing algorithms. These algorithms learn from the results to avail further improvement in the operation, this is called “learning on the go.”

To develop a capable learning model, Machine Learning utilizes the following algorithms:

Reinforcement Learning –

In this method, the machine depicts the best possible learning to make precise decisions. Developers train ML algorithms on a wide range of data to improve the decision-making for the environment.

Supervised learning

Here the algorithm enhances its learning from given data and accompanying target responses. Later when this data is presented with new examples, the ML algorithm can predict correct responses.

Unsupervised learning 

This algorithm learns from the given examples without any accompanying answers. This algorithm learns the data patterns on its own.

How to use ML to build innovative mobile apps?

If you are a mobile app development company or an app developer, then you must know how Machine Learning has changed the mobile app industry.

ML allows you to incorporate futuristic trends and technologies to make your application equipped with exceptional features but unprecedented capabilities as well.

How to use ML to build innovative mobile apps

Here are a few use cases of Machine Learning in mobile app development: 

Object and Face recognition

Machine Learning algorithms can help you integrate object and facial recognition technology into your application, which can enhance the user experience to a whole new level.

An ML-powered app can recognize objects and analyze human faces. Then, the app can leverage the ML algorithm to apply filters, overlay lenses, or masks using the smartphone’s front-face camera.

Face and object recognition features can be used in medical apps to determine a specific condition such as inflammation and swelling.

Mental health apps can use ML algorithms to analyze patients' faces to identify the symptoms of mental issues.

Improved Search capabilities

Search is the most common function anyone uses on daily basis, it helps users to find the intended information about a specific item, topic, or object.

The ML can optimize the search function in your mobile app to deliver far better and contextual results, which makes the searching process less tiring and intuitive for your end-users.

You can leverage the abilities of machine learning algorithms, that enable a mobile application to learn from customers’ preferences, browsing history, and past purchases, so that prioritized and personalized results could be offered to them.

The ML framework uses this data, to perform analysis to develop the logic based on user preferences. It helps you to improve the sales prospects of your services and products.

ML-powered Virtual Personal Assistant)

Virtual personal assistants are gaining momentum these days, as they offer an effective and cost-effective method of interacting with the end-users.

The objective is to handle the routine tasks usually performed by an assistant or secretary. It includes fetching user responses, taking notes, providing answers to routine questions, updating small information, or reminding the users about their appointments.

Incorporating ML-powered virtual assistants will certainly enhance the usability of your mobile app.

You can also integrate a voice command-enabled VPA into your mobile app. It will empower your customers to access your services via simple voice commands. Since the launch of Alexa (Amazon), Siri (Apple), and Google Now, the usage of virtual personal assistants has been growing rapidly.

ML-powered online customer support

Customer service plays an important role for any business. However, it comes with its own set of challenges, apart from the app development cost it may incur to your business, you may face resource issues as you cannot deploy adequate human operators, who can answer every user query round the clock.

You can use ML in your application to deploy chatbots to enhance your customer support experience.

An ML-powered chatbot can immediately respond to any user query at any point in time.

If the end-user is facing a complex issue, then the chatbot can connect them to your specialized customer support staff straight away. Such responsiveness will encourage end-users to use your application and keep coming back in case of any problems.

Accurate Fraud Detection

Mobile applications are highly prone to online fraud and security breaches. Machine learning can help you make your application framework streamlined and secured.

ML algorithms can effectively detect the patterns of financial transactions and decide if a given operation is fraudulent or suspicious.

It enables application developers and administrators to implement effective access rights, which ensures no need to monitor the application all the time.

Using machine learning, your application can comply with high-security guidelines and standards via continuous learning and automation.


Machine Learning technology is among the most cutting-edge domains of the software development process. ML Algorithm not only enhances the customer experience, but also increases customer engagement, improves customer loyalty, and so on.

Machine Learning algorithms enable your mobile app to analyze a large set of data and make accurate predictions.

Machine Learning has some amazing use cases in industries such as healthcare, banking, e-commerce, transportation, and education. Depending on your business requirements, you can leverage Machine Learning algorithms to develop a customized solution.

All you need to hire a seasoned team of ML experts and mobile app developers to craft a successful application for your business.

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