Machine Learning

How Is Machine Learning Used In Mobile App Development?

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Have you ever thought how Amazon just every time gives you the proper recommendations and suggestions on products to purchase?

Or, haven’t you got surprised when you discovered YouTube to following your preferences with their automatic listings?

Well, all these leading platforms prescribe content based on user intent. And all these platforms actively use machine learning technology to detect your purpose, preferences and context. 

So, if you build a robust mobile app, machine learning can play a significant role in augmenting your user experience.

Some of the key benefits that you should look for in an app based on machine learning include higher sales conversion, more opportunities for cross-selling and up-selling, the scope of addressing marketing issues, improving user experience, etc. When mobiele applicatie ontwikkelen, no business can undermine these huge promises of machine learning technology. 

Three different types of Machine Learning (ML) algorithms 

Machine Learning (ML) refers to a subset of Artificial Intelligence (AI) technology allowing faster and automated data processing to produce compelling data-driven insights.

ML is used to build robust software programs or algorithms to use this data processing power and data-driven insights. 

There are three different types of machine learning algorithms used by mobile apps. Let’s have a look at them one by one. 

  • Supervised learning: This type of ML algorithm learns from the examples of user inputs and responses. Based on the learned models, the ML algorithm comes with the accurate prediction of response.
  • Unsupervised learning: This type of ML algorithms makes the learning of diverse examples but not corresponding to any response. This type of algorithm can detect the data patterns without the help of reactions, for example. 
  • Reinforcement Learning: These ML algorithms are trained by developers to take particular data-driven decisions based on the environment. 

App Niches Where Machine Learning is Proved to Be Most Effective

Machine learning, as of now, has been proved to be a highly dynamic and multifaceted technology that several industries use. Let us now describe the utilisation of ML algorithms for particular sectors. 

Machine Learning into Banking apps 

Machine learning is widely used to garner predictive insights based on customer data for the banking and financial industry.

Thanks to the predictive insights based on accurate data feeds, ML in banking apps can accurately predict market risks and vulnerabilities, customer preferences and market dynamics. These predictive insights help banking apps to stay ahead of the competition. 

Machine Learning into Healthcare Apps 

Based on the insights drawn from the medical data, machine learning algorithms in healthcare apps can help detect vital medical signs and corresponding patient care manoeuvre that can derive the best outcomes and treatment responses.

Thus, with machine learning-based insights, healthcare apps can improve efficiency and boost the quality of patient care to a great extent.

Machine Learning into Fitness Apps

As wearable fitness and personal healthcare devices are continuously getting popular, machine learning in fitness apps can derive many valuable data-driven insights on health conditions and patient’s vital statistics.

Furthermore, machine learning through fitness apps can also learn from personal behaviour and habits and suggest more user-specific workout plans. 

Machine learning into e-commerce and retail apps  

Machine learning is now being widely used by e-commerce and retail store apps. By understanding customer behaviour and learning about their habits, machine learning-based e-commerce and retail apps can make automated product suggestions to drive more purchases.

Thanks to machine learning, e-commerce apps can make recommendations to drive more sales and business conversion. 

Machine Learning into car-hire apps 

Uber has been the flag bearer of advancing intelligent automation for their unique on-demand taxi hiring aggregator app.

Today most on-demand ride-hiring and ridesharing apps use intelligent automation powered by artificial intelligence and machine learning algorithms.

Learning over time about the average traffic condition, customer demand volume at different times of the day and fuel consumed by the vehicle, the machine learning algorithm can route on-road cabs, determine rates and determine the best routes.

Advancing Computer Vision

Computers are increasingly capable of learning about the content used and created using their processor’s thanks to machine learning technology.

Often described as computer vision now achieved never-before advancement and can interpret videos and images, track moving objects. Decode and interpret artistic visuals, etc. 

Image recognition as the subset of machine learning-powered computer vision can easily facilitate labelling and classifying images, organise them based on their integral patterns, ensure advanced visual search and utilise ideas contextually for different use cases. 

Natural Language Processing (NLP) 

Natural language processing (NLP) is a particular field of study that provides active help to computers to understand human communication in natural language and use that natural language in contact with human users.

This capability of developing an understanding of natural language uses is a subset of machine learning capabilities. 

The most significant outcome of NLP in mobile apps is personalised communication in a highly native language while using the tone and choice of words based upon user preferences and personality. 

Understanding Text Content  

Just as machine learning classifies images on different parameters, machine learning capabilities can also categorise text content and decode the context and meaning of content based upon previous knowledge about the text and its uses, context, source and intended communication. 

This machine learning-based ability can help an app understand customer sentiments deeper, classify text into different categories and reorganise text content for better readability and understanding of the end-users. 

Conclusion

There can be hardly any doubt that the future-ready mobile apps are likely to be more innovative and power-packed enough to automate many tasks.

Machine learning technology perfectly fits into this advanced avatar of mobile apps just because most jobs are to finish intelligently.

With the least manual effort, ML algorithms are simply unbeatable. Thus, in the years to come, machine learning will become deciding factor to push the growth of apps for intelligent automation in homes, workplaces, industries, transport and medical facilities. 

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