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How Machine Learning is Useful in Mobile App Development

In the mobile-centric world, having a mobile app has become essential for businesses. Mobile app developers are trying all the latest technologies and strategies to develop successful mobile applications. The popularity of Artificial Intelligence has grown so much throughout these years. Machine Learning, which is a subset of AI, has a huge impact on the app development industry.

Studies show that in 2017, the value of the Global Machine Learning industry was $1.8 billion and can reach up to $20.83 billion in 2024. Also, According to the article on analyticsindiamag, people spend $18.8 billion on AR/VR products worldwide.

Machine learning allows computers to work without being programmed. It enables the system to identify data patterns, make verdicts, and predict future interactions. For example, it can detect online credit card frauds, analyze the manufacturing process, and predict customer behaviour from previous data. Machine Learning uses data analysis and algorithms to develop an app that can interact with users, entertain them, and deliver a personalized customer experience.

Let’s see how Machine Learning is useful in mobile app development.

How Machine Learning helps in Developing Innovative Apps

In-app development, Machine Learning provides the universal solution for any web problems that may come. The algorithm or Machine Learning relies on continuous learning mechanics so that it can deliver an amazing customer experience. Currently, the majority of mobile apps are embedded with Machine Learning mechanisms.

Let’s see how it helps in developing innovative mobile apps.

  • Enhances Personalized Experience

As stated before, Machine Learning (ML) has an algorithm that can analyze user behaviour through their social media activities and uses this information to create a personalized experience. When the user starts using the app, it shows the relevant information and app ratings. A personalized experience gains more user attraction, resulting in more customer engagement and improved conversion rates.

  • Improves Search Experience

Nowadays, people look for solutions for their queries on the internet and expect to get the most relevant searches. Thus, an effective search has become essential to create an astounding user experience. Applications embedded with Machine Learning can easily and rapidly create an impressive search experience.

  • Improves Online Security

In this digitized era, stolen data has become the biggest threat for every online business. Biometrics, voice and face recognition, and fingerprint are some exclusive security features that help to build a strong security system in mobile applications.

Machine Learning has allowed apps to secure the data by app authentication. In addition, ML can track user’s transaction history, other activities and can provide additional security features, such as logistic optimization, business expertise, and product tagging automation.

Different ways to Apply Machine Learning in Mobile App Development

There are numerous ways mobile app developers can apply Machine Learning in app development. Let’s take a look at a few of them.

  • Data mining

Data mining uses mathematical algorithms to sort the data and evaluate the odds of future events. Machine Learning discovers the pattern, focuses on the database, predicts the future on that basis, and generates genuine information while developing the app.

Mining these data can identify various patterns that are required in storage, maintenance, and data analysis. Machine Learning algorithm observes user’s data, such as age, gender, social media profiles, etc., gathers them, and designs a highly customized app.

  • Tracking

Machine Learning helps in tracking financial data, as it is the prime compound in any business. It uses the transaction history of the users and presents them with exclusive deals and offers. Therefore, the customer receives regular offers, and businesses can deliver a customer-centric experience, resulting in more customer engagement.

  • Data Supervision

Data supervising and ML defence system improves app security, as it merges with mobile apps. Furthermore, machine Learning program modules are so well trained that they detect fraud activity and alert users immediately, thus, users can easily rely on these modules.

  • Search options

Machine learning displays the most accurate search result, even if the keywords don’t match. Moreover, the search results will show the relevance by observing user’s clicks, search history, search queries, and almost every user activity.

Benefits of applying Machine Learning in mobile app development

As we know, Machine Learning has a huge database, thus, if obscured correctly, developers can solve numerous complex problems and make an impressive customer-centric app. Here are a few benefits of machine learning in app development.

  • Application security
  • Customer lifetime value prediction
  • Prognostic maintenance
  • Eliminates physical data entry
  • Spam detection
  • Financial analysis

Benefits of machine learning for developers

In mobile app development, developers often get confused at improving the logic development, which requires a lot of time for amplifying and deploying the app. ML helps developers understand every aspect of coding by simplifying the logical development path.

It helps developers comprehend the latest trend and different mobile app development patterns and improve their coding expertise. Moreover, ML improvises some development processes if developers get confused at some stage and add a few commands that fulfil user requirements.

Additionally, developers can increase app security by implementing Machine Learning. It not only prevents spammers but also protects the app from external threats without explicit programming instructions.

Applications that have benefited from using Machine Learning

  • Netflix :

With the use of ML, Netflix has made an accurate and personalized user experience by using linear and logistic regression. As we know, Netflix has a huge database, from actors, users, reviews, timespan, categories, etc., to offer their users.

ML algorithms are trained by observing users' activities and behaviour, which creates an immersive experience for its users. For example, if a user has previously searched or seen a horror film, Netflix will recommend more movies in that genre.

  • Tinder

Well, Tinder is a dating app that uses its algorithms to find the prospective partner for its user. First, it uses information, such as photos, areas, interests, and many more. Then, it observes how many times they are swiped right due to some specific fact and reorders that fact to be the first thing others see.

For example, if a person has been swiped right due to a specific picture multiple times, it will rearrange their photos and put that photo first in the album. This feature increases the chances of getting an ideal match for its users.

  • Snapchat

Snapchat uses its algorithms for computer vision to find faces in photos and adds fun elements, such as hats, animal filters, glasses, etc.

  • Spotify

Spotify has three sets of ML algorithms to provide a personalized user experience. The first algorithm compares multiple playlists made by users to the songs they used to listen to and suggests similar songs based on their preferences.

The second algorithm works with natural languages and processes the song lyrics, blog posts, and articles about the specific songs and musicians on the internet. Then, based on this information, it suggests similar songs, artists, and playlists.

Meanwhile, the algorithm represents the audio model. ML analyses the data from raw audio tracks, songs with similar music that is popular among other users.


Machine Learning has improved the app development strategies and has changed the way of app development. The majority of app development companies rely on it for better performance, accurate research, methods, advanced authentication, and security.

Although Machine Learning has a few disadvantages, like overpowering the landscape of diverse algorithms and the requirement of a complete configuration set in the initial stage, it has become essential for the app development process, as you can rely on them for the most effective and reliable mobile apps.

About the Author

Jack David is an Assistant Editor at Mobile App development company - Sysbunny, where he crafts articles on AI, AR & Marketing Automation. When he's not writing, he loves to read & try new things.

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