Have you ever wondered how the shopping app automatically suggests the top pick for you? Or why YouTube shows you back-to-back related content based on videos you watched"? This is because these companies use ML in mobile applications. So let's find out why you should integrate machine learning into your mobile application.

Reason to build machine learning applications :

• After Machine Learning integration, 76% of businesses recorded higher sales

• Machine Learning technology predicts/Suggests better user behaviour, optimizes processes, to lead up-sell, and cross-sell

• Over half of enterprises are applying AI to refine promoting issues

• Machine Learning has helped several banks to increase new product sales

Types of machine learning algorithms use in mobile application :

Machine learning algorithms are divided into 3 main categories, these are follow

1)Supervised Learning:

Supervised learning is very simple to understand and easy to implement , In. supervised learning algorithm dataset act as a teacher . It's like teaching a child using flash cards , it only focuses on singular tasks like more data feeding gives you accurate results and by giving more feed the accurate results you get . It's used in advertisement marketing , mail spam classification, and like face recognition.

1)Supervised Learning:

Unsupervised learning algorithm is opposite to supervised learning , it relies upon what input u have given without output or without target variables. It does not have a teacher like supervised learning to correct the model . It cannot add labels to the data or cluster , suppose we have given a data set of mangoes and bananas it cannot create group of mangoes or bananas but it separates bananas from mangoes

Unsupervised learning algorithms based on data and properties, we can say its data-driven algorithms, its used in recommended based app like e-commerce, and tracks user buying habits

3)Reinforcement learning :

Reinforcement learning is very much different from supervised and unsupervised algorithms. , we can see there is relationship between supervised learning and unsupervised learning in terms of label which presence and absence in respective algorithms .reinforcement learning is very much inaccurate at the beginning it makes a lot of mistakes.

To understand reinforcement learning, take an example of a Mario game , to train our environment to play a game , we give it some set of actions which will affect the environment.

Mostly it's used in video games and simulation of any industrial actions.

The following are the examples of the above algorithms which used in a real time application nowadays

Transportation mobile applications:

Google maps is the best example of machine learning applications , it provides up-to-date information about current traffic conditions based on current situation app optimize road traffic jams, ML based on time estimation to reach the destination based on the toll jams, traffic conditions, and average speed

E-commerce :

In Online retail mobile apps such as giant ecommerce apps, use machine learning algorithms in several ways. Such algorithms help buyers to buy relevant products ,combo-offers, Recommendation based on previous history and search.

Conclusion:

Machine Learning algorithms improve customer experience, maintain customer loyalty, increase engagement, and so on. This technology can use in several mobile application to improve better user experience and data analysis