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Mary Smith

Augmenting The Mobile Applications With Machine Learning Technology

Do you often hear the term Machine Learning?? Wikipedia has given a very simple definition for this term. It states that: “Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.” In fact it is considered the future of technology and can offer tremendous value addition to the Mobile Application.

The term ‘Artificial Intelligence’ often comes up whenever we speak of ML. But these two terms are not exactly the same but there does exist a line of connection between the two.

Artificial Intelligence refers to the intelligence exhibited by machines and perform task that normally requires human intelligence. Machine Learning Technology is a current application of AI that is build around the idea that we must give machines access to data and let them learn for themselves. MI tools and applications are gaining wider acceptance in various popular ecommerce brands. Retailers like Amazon, eBay and consumer product sites like Lenskart are using ML algorithms to manage several aspects of their business.

Let us dig deeper into the different ways by which ML can be incorporated successfully in Mobile Applications.

Some basic functionalities can be simlified if we allow machines to start performing the tasks themselves using their own intelligence. This will definitely save a lot of time and remove human error too. The point is how this very technology can be incorporated into our mobile apps? The following points will answer our query.

Product Search:

ML tools aid us in query understanding, ranking, favorites, expansion etc and provide users with more relevant information when they are searching for products. Informations are drawn from behavioral data with searches, search history, semantic outcomes and a user’s portrait etc. to create sub groups and match with the user query.

Product Recommendation:

These are built around filtering methods, site content analysis, purchase patterns, user behavior and also the business logic, brand implements etc that make searches more relevant.

Forecasting Trends:

It is crucial for e-commerce brand to continuously study the changing trends and react quickly with matching products and services. Big Data and Machine learning can merge these trends with sales information from different sources (social media, digital reports, blogs, etc.) to make predictions real-time.

Fraud Detection and Prevention:

Reports suggests that the e-commerce industry is crowded with frauds and con stars. Machine learning technique build defense systems that improve ongoing monitoring and trigger alarms.

Summing Up The Key Points:

  • Developers need to concentrate more on data mining to get relevant output.
  • Search for the most appropriate yet simple and easy ML Method.
  • Focus needs to be on the methods of data collection.
  • Make use of a specialized data scientist to give an extra boost to the project with his useful insights.
  • Better understanding of data features and updates to understand the predictability and the brand’s learning process.
  • Machine learning algorithms requires thorough testing before implementation.

Conclusion:

IMAGINE!!! A world where Robots or humanoids cooking our favourite dish or cleaning the house for us.

Does it sound like a scene from a famous Sci-Fi film- i.Robot??? This is can be a reality. The basic concept of Machine Learning can convert this imagination into a reality.

Yes! that distant future is nearly here. So Buckle Up Guys!! You are about to enter the Machine operated Modern Era.

As Pedro Domingos rightfully quoted:

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

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