Tips on using Machine Learning in Mobile Applications



Machine Learning (ML), a sub-application of Artificial Intelligence (AI), is the ability of a system to improve and learn from its experiences without being explicitly programmed. In simpler words, it focuses on the development of computer programs that can access data and use it to learn on their own without any human intervention. There are several machine learning courses that enable one to incorporate ML tools in the development of mobile applications.

In recent years, machine learning has evolved its applications in various areas, ranging from speech recognition, web search, to self-driving cars. In addition to these, Machine learning is now being incorporated into many mobile applications to provide a personalised experience to their users. How then, can one use the benefits of machine learning to augment their mobile applications? Following are some tips that you can follow for using machine learning in your mobile applications:

Incorporate Individual Approach:

If you ever wonder how Netflix comes up with relevant recommendations for the next TV series for you to binge on, machine learning is the answer you’ve been looking for. Machine learning (ML) can be utilised to make the application appealing for each user by incorporating an individual approach. This will allow the app to customise content for individual users based on their personal preferences. The users are thus provided with relevant content that’s based on their interest and lifestyle. Apps like YouTube, Instagram also work on a personalised individual approach to increase user traffic.

Faster and Efficient Searching:

Machine learning can help users search for information more efficiently through the various tools it has to offer. Users can be provided with the most relevant information by adding filters like spelling correction, ranking, suggestions, voice searches, etc. Machine learning can thus help to make the searching process far more intuitive for users.

Personalised Recommendations:

If you’re into e-commerce, then you can incorporate machine learning into your app to benefit your business. Generating recommendations for your users based on their purchase pattern and search requests will show them products that they are most likely to be interested in, which would help drive your sales. Amazon and Flipkart are some examples of mobile apps that focus on individual customer approach to increase their sales by influencing buying decisions.

Word Predictions: 

Tired of always correcting your typos? Machine learning tools can be used in predictive texts to predict the next word a user is most likely to type. The neural spatial model makes use of the user’s typing history and matches it with the word currently being typed by the user to make its prediction. This facilitates faster typing and fewer spelling errors. Google’s GBoard is one such example of predictive text, which also enables users to find the right emojis by drawing an outline of the same.

User’s Assistant:

The next time you talk to Siri or OK Google, you can ask them a question or two about machine learning as well. A lot of application developers are using artificial bots which incorporate ML algorithms to deal with customer queries. These digital chat assistants provide customers with the information they need by making use of the repository of data that is gathered in their systems.

Financial Assistant:

If you feel that you need help managing your finances, look no further, for machine learning is here to assist you. ML techniques can also be incorporated in mobile applications aimed at assisting users with their finances. With the use of ML algorithms, the mobile app can track and observe an individual’s spending habits to make suggestions on making smarter money-saving decisions. Such apps understand the behaviour and lifestyle of the users to generate a personalised expenditure plan that helps the users to save more money. Applications like Walnut and Oval Money are examples of apps that provide such financial assistance.

Education Assistant:

Machine learning tools and techniques can be incorporated into your mobile application to assist students in understanding the academic concepts. The ML algorithms can be used to design chatbots that answer students’ queries, check assignments, and keep a check on the students’ grades and assignments. These bots can collect real-time data to suggest personalised learning plans for students.

Fitness Assistant:

Machine learning algorithms can be used by fitness app developers to provide a new dimension to their apps. Merely listing exercises that one can perform has become a digital passe, machine learning is now being used to customise apps based on specific needs and habits of the user, ranging from sleep pattern to dietary habits. Based on the customer’s inputs regarding aims and physical state, machine learning algorithms can generate a personalised training program, thus effectively acting as your very own personal fitness assistant!

Health Assistant:

Incorporating machine learning algorithms in mobile applications can help provide users with interactive healthcare assistance as well. The ML algorithms can be designed to access healthcare databases pertaining to various diseases to help diagnose health-related issues, and can suggest users the necessary steps to be taken for treatment and medication. These apps can also use machine learning tools to keep track of blood pressure and sleeping patterns to make personalised suggestions. IBM’s Watson provides a similar service to cancer patients which uses its extensive database to provide an accurate diagnosis of the disease.

Battery-saving Suggestions:

Reduce the amount of time your phone spends connected to the charger by incorporating Machine learning techniques to suggest battery life-saving recommendations. The ML algorithms can be designed to collect real-time data from the device to analyse usage patterns to provide users with useful tips that could save their phone’s battery life. Carat by Carat Team is an app that uses ML tools to provide personalised battery-saving recommendations. This app also reminds its users when any other app malfunctions and needs re-downloading.

Security and Authentication:

If you’re amazed by Apple’s latest face recognition feature, machine learning is again to be thanked for! Machine learning is being actively used in user identification and authentication processes through the use of recognition techniques. Apple’s biometric recognition, and recently, face recognition softwares are stellar examples of how machine learning can be used for user identification, without the need of manually keying in a password.

Fraud Prevention:

You can use machine learning to protect your user’s data against malware and spam.  Machine learning algorithms allow your app to monitor ongoing processes in the background without the need of constant control by the users. This allows the detection of any suspicious activity that could potentially pose a risk to your data. While traditional apps only prevent the user’s information from threats that are already known of, machine learning can protect user data from previously unidentified malware attacks in real-time.

It is evident that machine learning algorithms can be used in a variety of ways to provide users with improved mobile application experience. The implementation of ML tools can thus help one in setting their mobile applications apart from others.


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