Machine Learning in Mobile Applications

Do you ever question how Elon Musk gets the Tesla to drive on its own? Have you ever wondered how betting applications work? Or how some finance applications can derive investment predictions from the simple raw data imputed into them? Don't fret! It is not some miracle happening, it is only Machine Learning. Yes! You read that right, computer programs are now being developed to learn. So, let's get down into the intricacies.

Machine Learning is simply the deliberate programming of software applications that gives the system the ability to learn and improve on its own by drawing out patterns from data imputed without explicitly directly told to do so. In short, Machine learning is an artificial intelligence algorithm deliberately programmed into software during development to make computers "think" for themselves by developing sequences and undirected concatenations with no directions given to do so. It involves improving a computer system by programming it to learn from experience and improve their performance. This is essentially how betting prediction application works.

The use of this subclass of Artificial intelligence has skyrocketed technology into advanced levels and now, even mobile applications have begun to utilize machine learning. Mobile application developers use ML algorithms when building Android and iOS applications. Machine learning in mobile app is therefore not only peculiar to iOS as most will conclude but is also quite common in Android applications too. This trend of both machines learning iOS and machine learning android apps is so prominent; you see traces of the algorithm in your daily life. Filtering out messages and timeline suggestions are day to day machine learning applications to the mobile world.

In Android and iOS phones, several applications use ML for functionality improving the user interface of these applications and customizing use to fit phone owners.

The personalization of mobile application to the users' tastes, preferences, searches, location, and dislikes are all attributed to machine learning algorithms that have been embedded in different software. Not just personalized applications, but also businesses use this for branding and developing strategic plans for their progression and marketing.

Although there will be a few tweaks and changes, machine learning android applications are quite similar to machine learning iOS applications. There are common applications present in both which work the same way in utilizing ML structures.

Here are a few mobile applications on both Android and iOS operating system and their methods of manipulating ML to fit into their business goals and create a more user personalized experience for customers:

The quest for love online and meeting your soulmate is the one problem in millennials' lives Tinder hopes to play fairy godmother on. But how does the fairy godmother actually pair up soulmates together and make the perfect match? There is no surprise there that it is through Machine Learning!

With ML algorithms, the measure at which your pictures in your gallery gets swiped left or right is used and manipulated to come up with potentially suitable matches. Simple codes translated to make the application choose Mr (s). Right is why millions and thousands of people around the world subscribe to this service Tinder provides.

The rise of Siri and Google Assistant was a game-changer in smartphone technology. The creation of these personalized virtual assistants brought about a whole new market for the companies. Phones have so far become cocaine with millions of people addicted to their devices.

Virtual assistants work with voice recognition and are designed to work according to their owners' specifications. Reminders can be made on daily activities and these data collected and stored improves the usability and purpose pf the assistants. From tasks given to Siri or GA, your search result, news preferences, and daily tasks are reorganized to suit your taste. All of these tasks and features only possible due to the presence of ML models within the application codes.

Customization of e-mail ads, detection, and blocking of spam mails, distribution of mails into different folders like social, updates, and promotions are due to the email's modification to learn adapting to user preferences and recognizing patterns of incoming texts. The machine learning abilities the mail has in conducting all these tasks are deliberately designed to make your life easier. Instead of users clustering through hundreds of unnecessary emails before finding important updates, the email software is designed in ML algorithms to distribute the mails in the order of importance.

Spams from the suspicious website and email addresses are filtered through Multi-Layer Perceptron or C 4.5 Decision Tree Induction ML models. The models can detect malware and filter them out. This makes email services reliable and highly regarded by the majority of signed up users around the globe.

Do you realize that suggestions of friends on Instagram and Facebook are common ML attributed task? Every search or query you make on any social media platform is data for the software. These data are translated through machine learning principles into information by the application and suggestions are made for you. Even advertisements popups are basically suggested from the data gotten through the types of products you like and the pages you frequent daily.

Using social media has transformed from just connecting to the world and changed into the algorithm and lines of codes within the social media backend building blocks bringing the world to you. All of which is possible because of Machine Learning applications.

The utilization of Machine learning in apps altered the whole business world. Every text, search query, and information typed into search engines, apps, and social media pages have become data used to personalize and make a better app for users. The importance of machine learning in apps cannot be overemphasized. Businesses can change the game of data analysis for strategic planning and sales approach. Machine Learning abilities can not only specialize app commands but take simple data and convert them into productive information without unnecessarily coding for such results.

Not only restricted to search results on social media, but a more complex machine learning application is Snap Chat. Snapchat uses both "augmented reality and machine learning algorithms" for face recognition and filters. The ability to see between color contrasts and recognize a face, knowing where each facial feature is in order to apply filter properties is the algorithm property that makes ML unique

Data analysis is intertwined with the functionalities machine learning offers. Data is simple in its form and is generated by applications daily. Each command made on a mobile application is data generated. The multiplication of each command by thousands of users cut across different countries and nations makes for bulk data known as "Big Data". This big data contains queries, information specific to clients by age, location, sex, and even financial expenses made on mobile stores. The conversion of these Big Data into useful information is what Data Analysis is all about. In today's world, whoever can understand the customers better is one's privy to receiving the most sales and getting ahead of the game.

Data analysis application is very important and vital to the success of any mobile application. Understanding the features that are most preferred by customers is quite vital to improving UIX. It is almost impossible for updates on the app software to be made without all this information made. Customization of ads by the customer is only possible by analyzing the activities of the users on the mobile application. This is where Machine Learning in mobile application comes in.

For there to be a proper understanding of your customer base and your user preferences, ML algorithms must be added to the development. Machine learning is a tool that indeliberately pinpoints the sequence and the presence of patterns in users' activities which in essence "data analysis". Big data analysis is only possible at a fast pace in real-time by the actions of Machine Learning. For improvement in the stability and reliability of applications, the large pool of data derived from customers has to be analyzed in real-time. This way, there are no surprises and unforeseen declines.

Machine Learning is one very essential tool used in data analytics for applications. Recognizing your customer's needs through the data generated from monitoring real-time usage will have an effect on downloads and ad to sales conversions. Maintaining a good business with a mobile application begins with development. The development of mobile applications has to essentially incorporate in one way or the other, machine learning algorithm to make use better.

What separates Netflix from Hulu or HBO Max, isn't the frequency of new film release. It is the ability of the Netflix interface to filter out movies by customers' preference and search queries and hence create a better experience for their users. YouTube app remains one of the world's biggest video platforms because of the recommendations abilities of the application. All of these cited examples are all machine learning in apps that are at the top of their games in data analysis and technology advantage.

So, if you are therefore wondering if your app needs a machine-learning algorithm to ascertain its highest level of functionality, the answer is Yes!