Machine Learning (ML) is a branch of artificial intelligence and computer science that gives computers learning capabilities through algorithms and improves its ability to conduct tasks and make more accurate predictions based on the data it collects.
Machine Learning plays a vital role in our everyday lives and can be found almost everywhere from search engines and eCommerce sites to smart devices and digital assistants. It can even be found directly on your smartphone and the apps you have downloaded on it. This small device utilizes machine learning and assists you in a variety of ways ranging from voice assistants to learning your daily routine and providing you the information you need when you need it. Many businesses use it to improve the user experience of mobile applications.
But a common misconception among most people is that machine learning is highly complex and requires rocket science to implement in mobile apps. As a result, many small app developers do not make use of ML in their apps despite the many benefits it brings to the table. But thanks to large tech corporations like Apple, Google, Microsoft, and even Netflix and Amazon, incorporating ML into mobile apps has not only just been made easier, but also benefactor in the app’s user experience for consumers.
Machine Learning in Mobile Applications Apple App Store and Google Play Store
All smartphone users have opened their mobile operating system’s (OS) app store to download the apps they love and use most. However, behind the scenes of the smartphone’s app store, machine learning is applied to collect data on what apps the user downloads. This can then be used to make recommendations on what apps the user can download based on the apps they have previously downloaded – in other words recommending based on the user’s taste. This is called a recommendation system. For instance, if a user downloads a social media app like Facebook from the Google Play Store, then it may recommend other social media apps like Twitter and Snapchat, or other apps developed by Facebook like Instagram and WhatsApp.
Snapchat feed and filters
Snapchat is a central social media platform that enables users to communicate with one another. A user’s feed on Snapchat will not only show stories from people they follow but will also show some recommended articles and stories based on how relevant it is to the user. This is done via a recommendation system that uses machine learning and the relevancy of the results will be based on data collected from the user’s interests and what they previously viewed. Alongside that, Snapchat filters use facial recognition and machine learning to identify and learn the appearance of the user’s face in order to ensure the filters are applied in the best way possible.
Digital Voice Assistants
Digital voice assistants like Siri, Alexa, and Google Assistant are widely used by many with smartphones. They allow a user to ask it questions or do a specific task with just the user’s voice. Machine learning is a key part of making sure these digital voice assistants understand human speech and accurately and effectively conduct it’s given task. Not only does machine learning allow for speech recognition so that it can better understand a user’s voice, but for every task, the assistant is given, it uses that data to derive patterns to predict what your next task may be and complete other tasks faster.
Numerous e-commerce’s, such as Amazon, strive to give recommendations that match the user’s request and search history. Like the smartphone’s app store recommendation system, as a user searches for a specific product on an eCommerce site/app, the data from that user’s search and what they viewed is collected via machine learning. This will then be used to make specific product recommendations that may fit their preference either the next time they visit or through push emails/notifications.
Tinder is a top-rated dating app that assists people in finding the ideal match for a potential partner. In order to give user’s a chance at finding the perfect match, it uses a machine-learning recommendation algorithm to collect data on a user’s decision of previous profile recommendations and predict which profiles could become an ideal match for the user based on the collected data. The user will then be presented with the most popular profiles based on the collected data, resulting in improved recommendations and a better chance at finding your potential soul mate.
Movie and TV show streaming apps, such as Netflix, also use machine learning algorithms such as linear regression and logistic regression. Through a recommendation system that collects data on movies and shows that a user has previously watched on Netflix, they are recommended related films based on their taste and what the algorithm thinks the user would like.
This provides a better viewer experience to the users by allowing them to view shows and movies they otherwise would have never found on their own. The content is recommended based on the user’s tastes as well as those of other users with similar tastes.