Smartphones have changed our lives in many aspects and many sectors. Mobile applications have made our life easy and fast going. More and more mobile applications have been developed day by day, and with this, the demand for advanced and trendy mobile applications is also increasing.
Users are willing to have a mobile app that uses Machine Learning in it. In addition, some favorite apps are using ML, and users love those apps, so the demand for ML used in apps is increasing.
Machine Learning is the subfield of Artificial Intelligence. ML is changing a vast number of industries by accompanying mobile apps.
Reasons to implement Machine Learning in mobile apps
Think about the apps you are using every day. Starting from the morning when we start our day before going to our bed at night, we use multiple apps. Implementing Machine Learning in mobile apps has boosted up the user experience.
Given below are few reasons to implement Machine Learning in mobile apps:
Machine Learning for mobile apps
Mobile app developers can get a lot of benefits with the help of the transformations and updates that Machine Learning provides to the whole industry. This can be possible because of the technical capabilities mobile applications have on the table that enables smoother user interface, experience, and empowering business with outstanding features like providing particular location-based suggestions.
Users want their app to be personalized these days, so it is necessary to develop a quality app, but you also need to make sure that the targeted customers will stick to your application.
Here Machine Learning can help you to redesign your mobile app as per the user’s requirements. Machine Learning app development company is looking forward to developing more ML-based mobile apps after the success of many ML-based mobile apps.
Some of the top Machine Learning mobile apps:
7 tips used to implement Machine Learning in mobile apps
Here are the tips that you must know to implement Machine Learning in mobile apps.
1. Machine Learning-based mobile app to provide customer support
It is difficult for a human to sit behind a desk and answer the queries asked by millions of users. But this can be possible by using Machine Learning to deploy a chatbot as your online customer support 24/7.
A chatbot is a type of chatbox where users can ask any query at any time that a robot will answer through your mobile apps. This can help to respond to millions of users at a time. If the answer is tricky, the chatbot can also connect the user to a live agent who will interact with the user and give the appropriate answer to them.
As per a study, the market of chatbots is gradually and rapidly increasing by the passing years.
There is one new exciting feature of the chatbot that will recognize the customer's writing style and understand their query; after that, it will give the solution that is relevant to it.
2. Advanced search
Machine Learning can enhance search in your mobile application. You can deliver more contextual results in a better way. This can make your site more exciting and less tedious for your customers.
This ML algorithm enables the app to learn from the customers browsing movements and prioritize results that seem to be most compatible.
The ML framework analyzes this data further and forms a logic that is based on user desires. This way, you can promote your products easily.
For example, tinder is the best example of an ML-based mobile application and number one dating app that has broken all user satisfaction and obligation records. It expands Machine Learning to understand the user’s decisions and preferences.
You can now update your mobile applications with spelling corrections and voice searches. According to a report, almost 72% of people who use voice search are so involved that it has become a part of their lives.
3. ML-based virtual personal assistant (VPA)
Visual Personal Assistants gravitate to interact with the user automatically. The idea behind this is to make them complete the task that a secretary completed. This includes reading text or an email, taking dictation, scheduling, going through phone numbers, or reminding them about their appointments.
When you install a VPA in your app, you enable the customers to handle the features of your app with voice commands. Since the launch of Siri by Apple, personal assistants for every individual user have grown exponentially. After that, Google has also launched its assistant known as Google Assistant and Alexa.
4. Accurate fraud detection
Machine Learning can plan and can secure the overall app framework. In addition, it enables app developers to resolve the rights of their users.
There is no need to monitor the app regularly. The ML algorithm can detect the fraud and remove it on its own.
Many apps like uber are using ML to check the customer's previous transaction to avoid fraud in the future. It also uses face recognition technology to detect the customer's face and can also identify who is using the stolen card. This advanced security is possible because ML has empowered developers to integrate features like:
5. Object and face recognition
The fantastic facial recognition of Snapchat has never lacked to astonish users. First, it investigates a familiar face to start recognizing the face through its features. Then by using the Machine Learning algorithm, it can overlay the lenses, filters, masks via the phone’s camera.
Your app can get reliable with face-recognition technology. Some medical applications use face recognition to identify problems as they can recognize conditions like swelling and inflammation. In addition, there are many medical apps that recognize the mental condition of patients through face recognition.
6. Data mining
Data mining allows the mining of big data and the analysis of practical and non-obvious patterns and connections with significant data sets. It includes data storage, maintenance, and actual data analysis. ML provides both a set of tools and the learning algorithms necessary to find all possible connections within the data.
7. Healthcare apps
If you are ardent about healthcare apps or have a business related to health care, you must use Machine Learning. It can track your health, heartbeat, blood pressure, oxygen, all these using sensors.
Many other tracking applications can track the number of steps in a day, water intake tracker, activities, and use the data from many users with diabetes to learn and guide them with the data and remedies, and even medicines.
We all know the benefits of Machine Learning in many industries. These industries have helped in many ways to make our tasks easier and intelligent. In this blog, I have mentioned 7 tips where you can use ML in mobile apps.
As the use of ML apps is increasing and demand is also increasing, the mobile app development company should make sure to provide Machine Learning app development services to run the app smoother.
I hope this blog will help you get ideas and develop a mobile app based on Machine Learning.