The Android application market is brimming with apps that offer practically every possible service to its users. However, when it comes to user adaptation, few manage to survive till the end. Not only do users demand ease of functionality, but they also expect their app experience to be entirely personalised and in line with their preferences. Hence, it is not just enough to build a quality application; it should also convince people to continue using it in the future.
When it comes to Android development, machine learning (ML) techniques can offer some value-adds to your application in the form of recommendations and promotions, optimised search, forecasting, analytics, and many more. Hence, by enrolling in machine learning courses, Android developers can streamline their applications for the better in the following ways:
There is no better way of optimising your application like giving your users personalised recommendations on the content that they consume. This is made possible by machine learning techniques like linear and logistic regression. You can use these techniques to sort your content by reviews, genre, year, and other specific factors that characterise your app content. By sorting your content based on individual user preferences, machine learning algorithms can come up with relevant suggestions for your users, thus providing them with an advanced app experience. For instance, audio and video streaming platforms like Spotify and Netflix observe user interactions and then come up with recommendations based on a user’s style.
ML solutions in Android app development streamlines search results for your users by delivering them contextual and improved results. This makes searching less burdensome and more intuitive. The ML algorithms learn from a user’s historical search queries and filters results by prioritising on the type of answers that have mattered most to the user in the past.
This ML-powered cognitive search algorithm also separates search results by grouping them into FAQs, DIY videos, group articles, scripts, documents, etc., into a knowledge grid. You can optimise your app to collect customer information like search histories and the following reaction. By using this user behavioural data, the ML techniques are employed to provide users with more calculated and best-matching search results. For instance, Reddit makes use of a similar ML algorithm to improve the search result performance for its community members.
Predict user behaviour:
Machine learning techniques not only make your Android application more desirable, but they also help developers to predict user behaviour. By scrutinising data based on users’ age, location, gender, frequency of usage, search queries, etc., ML algorithms group users into segments and optimise app functions according to individual behaviour.
By making use of such predictions, you can upgrade your app such that it is more desirable to your users, thus making sure that it remains trending. For instance, Amazon’s suggestion mechanism studies the user engagement on the app, and an ML algorithm keeps optimising content based on the user’s likes and dislikes.
Not only does machine learning provide you with a valid recommendation system, but it can also add a layer of security to your Android app through a series of authentication procedures. Machine learning allows you to implement audio, video, and voice recognition features in your application, thus making it possible for your users to perform authentication through their biometric data, like fingerprint and face recognition. Machine learning also helps you in determining access rights for your app users. Many payment services require users to authenticate themselves through their biometric scans.
Machine learning allows you to do a lot more when it comes to your application’s security. You can also design ML algorithms to regularly monitor your app in a way that can detect and block malicious activities. While traditional apps can only act against threats and bugs reported by users, machine learning goes a step forward by automatically protecting app users from malware attacks, all in real time.
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Machine learning techniques allow you to effortlessly integrate features like customer support, user engagement, and interactive interface, thus giving your users an incentive to engage with your app more frequently. ML systems analyse large data sets, sort them into segments, and make real-time decisions about each section that work on increasing user engagement. Users no longer have to write emails or make long calls to get things done. You can implement machine learning to build conversational artificial intelligence chatbots to act as a virtual assistant for your users. Not only does this gives them ease in navigation, but it also saves users’ time by performing mundane tasks for them. You can also use machine learning in your Android app to build an auto-translator, thus giving your app a significant competitive advantage in the demographics of multiple languages.
Through features like top-notch search mechanism, a personalisation engine, secure authentication, machine learning can empower your Android application to perform better and give more to its users. As a result, the ability to integrate machine learning with Android app development is one of the most sought-after skills in an Android app developer. Machine learning is still evolving as a concept, however, considering the unique features it provides, investing in a machine learning course can significantly boost your career in android development.