Machine Learning | Fonseka Innovations
Machine Learning

What is Machine Learning?

If you are wondering what machine learning is, it is a method of analysing data and automating operations based on the provided data. This is commonly known as artificial intelligence, but the main core of machine learning is identifying basic patterns and mapping it with the relevant decision.

According to Accenture, a business can boost productivity up to 40% with the use of machine learning or artificial intelligence tools. With the uprise of the technology giants of the world, the digital marketing approach needed more personalised and efficient ways to attract customers. Machine learning covers the gap between the business and the customers.

It will help businesses improve in their reach and growth as it is purely on data gathering and learning process done by the ML (Machine Learning). It uses its pattern matching and unsupervised learning to map the right customer to the business.

Businesses such as; Uber or Uber eats uses this tool to estimate the time, cost and the best driver for that users’ preferences. With analysing real-time data, it manages to find the best options. This enables the business to enhance their services and increase productivity as an organisation.

Machine Learning Applications | Fonseka Innovations
Machine Learning Applications

How Machine Learning will boost the Application

Personalise your application’s user experience

Machine learning optimises data from a continuous learning process. It reads data and categorises users based on their preferences, what they are after, and what features they are interacting with a base.

With the proper analysing tools, you as a business can;

  • change the user interface to satisfy their needs,
  • enable features that the users want,
  • recommend products based on the user data (gender, location, interest, etc…),
  • change tone of content based on the user,

Optimised advanced search

One of the main problems with the application is to showcase or populate relevant result from the search feature. The main reason Google produces the relevant results is that it uses machine learning to understand the user and populate results accordingly. Therefore, one major boost you can do to the application is to enable machine learning to optimise the searches the users make. This will make sure to prioritised results or to show what the user needs. You can rank search cases based on the behaviour of the user and learn from the users’ point of view to map it with the relevant queries.

Analyse user behaviour

As mentioned in the previous point, you can gather and analyse user data to give them a customised user experience. Not only that, with the data collected, the business can show relevant ads, suggestions and promote products in a smart way.

As an example, if the user is into tourism and is interested in buying travel kits, with the knowledge gathered the algorithm can produce advertisements, links of the site to improve user engagement and promote tourism based adds and searches.

Enhancement of security

At present, the security of an application is one of the major issues faced by many organisations. By gathering user data, the business becomes a target for many hackers to steal data. Therefore, as a business, security measures should be high. Most people think having an encrypted password will protect their data. Based on statistics more than 86% uses weak passwords and/or use the same password for all their accounts.

Find out if your password has been compromised here

Machine learning is not just for marketing or engaging users with the application. With the proper tools used, the login or encryption can be placed to enhance security.

  • Image recognition can prevent many fake accounts or duplicate entries in the system,
  • Geolocation and activity mapping can prevent brute-force attacks or unauthorised access from other locations.
  • Wallet optimisation can keep an eye on fake credit cards or prevent suspicious activity in the payment gateways or transactions.

Best platforms for integrating Machine Learning in Applications



Dialogflow is a platform, created by the Google development team. This platform incorporates Google’s machine learning and its cloud products. By using this platform Google lets you use the Google Assistant to improve the application capabilities, such as; having voice chatbots, text-based chatbots. Most importantly this platform is user-friendly, intuitive and uses natural language processing to enhance your application.

This platform is a bit similar to Google’s Dialogflow as this gives the capabilities in natural language processing for applications. This can be used in many platforms and can integrate with bots, mobile applications, home automation devices and many more.



Tensorflow is an open source library by Google. This platform is an end-to-end machine learning platform which is a model-based library. This is a robust and powerful tool for analysing and for using advanced searches.

IBM Watson

IBM Watson
IBM Watson

The IBM Watson system is an ideal solution for search requests as regardless of the format, the collected data will be analysed in a faster manner. The solution can be used in many platforms and can have multiple approaches to produce the proper data with complex logical chaining.

Microsoft Azure

Microsoft Azure
Microsoft Azure

Microsoft Azure is a cloud solution from Microsoft. This platform is based on R and Python and has large community support and multilingual documentation. It has advanced analytical mechanisms and generates accurate forecasts.

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