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5 Machine Learning Algorithms, Changing the Game of Mobile App Development

Posted in Technology/Software

Machine Learning Algorithms And Mobile App Development Market

Machine Learning

Machine Learning Algorithms, Changing the Game of Mobile App Development.  Yes, 10 years ago, with the launch of PlayStore, a sequence for a better human future had been initialized. And the mobile app revolution we all are aware of is just a small part of it; more insurgencies will hit human life in the near future.

Because the partially evolved mobile app development market has stimulated a few snoozy technologies to be evolved fully.

One such technology is machine learning which has been into existence for quite a good time now but earned heed only after the rise of the mobile app development market.

As of now, almost all app developers are trained to develop machine learning-enabled mobile app because that’s what people and app owners want.

A machine learning-enabled mobile app takes the user experience to the next level and makes sure they are rewarded with only those contents or services which they actually want.

However, to actualize the machine learning-enabled mobile app, one should have the knowledge of machine learning algorithms and its usability.

In this blog, I will share the top five most usable and popular machine learning algorithms which can make an app stands out.

But before that, let’s understand the difference between machine learning, AI, deep learning and the use of machine learning algorithms in the mobile app.

Difference between Machine Learning, Artificial Intelligence, and Deep Learning 

Machine Learning

Machine Learning, Artificial Intelligence, and Deep Learning have always been treated the same and they are. But still, there are some disparities between them that are worth to know for someone who is bringing one of these into practice.

Artificial Intelligence, as the name suggests, can give the ability to machines to react like humans. Whenever, an unmanned machine solves the problem, based on the stipulated rules, this intelligent behavior is called Artificial intelligent.

While machine learning can take out the meaning from big data or historic data, make predication and enable machines to learn by themselves.

In other words, ML is the best tool to analyze, understand and identify the patterns in the data. It is a subset of the AI and people don’t bother to treat it differently.

Whereas, deep learning is a part of a broader family of machine learning which can be taught to accomplish the task for a machine by solving a problem in a way our brain works.

An artificial neural network is a major method to satisfy the goals of deep learning.

The major difference between machine learning and deep learning is related to its ability.

For example, if you want a machine to identify whether it is a dog or a cat, in machine learning, you have to manually add features or attributes like skin color, hair density, or height.

And in the deep learning method, the algorithm itself defines the features or attributes and gives us the result.

Even though, machine learning concept among these three is the most popular within developer communities as it helps them to understand and deploy it in the easiest way. They can derive many benefits using machine learning algorithms into the mobile app.

Benefits of having machine learning-enabled mobile app  

For the first time, the term machine learning was coined in 1980. But due to lack of high end performing computers and storage options, it didn’t get popular until early 2000.

But as of now when high end performing computers and large storage options are no longer as rare as flying pig.

All industries including mobile app industry get the opportunity to collect the data, process the data and find the patterns in that data for the sake of making data-driven decisions and offering pleasant user experience.

Several machine learning algorithms keep a close eye on the users’ behavior and based on that behavior, it predicts the users’ interest.

This interest is then used to keep the user engagement rate high, by showing them the things which they love to see, which fall under their interest radius!

TikTok And machine learning technique

One of the most popular apps, TikTok is working on the same machine learning technique. In the news feed, it only shows those videos which a user wants to watch.

Another use of machine learning can be seen in the admin panel. Generally, an app collects a large number of user and usage data every day.

Machine learning algorithms scan this data and find the business and usage patterns between them which then be proceeded by BI module and shown to admin.

The output contains very detailed information about the business. Thanks to this information, an app owner does not need to rely on his gut feeling to make vitally important business decisions that might decide the fate of the business.

In addition to this, an app equipped with chatbot and voice recognition features can only be possible to develop if the developer accommodates machine learning algorithms in the app.

So, now when you know the difference between AI, ML & DL and the benefits of machine learning-enabled app, let’s move ahead and discuss the top 5 most popular and useful machine learning algorithms in detail.

Top machine learning algorithms for mobile app development 

Following is the list of top 5 most usable machine learning algorithms which I have actually implemented at my company in several app development projects.

1.  Genetic Algorithm 

Genetic Algorithm which was introduced by John Holland in 1960, works on Darwin’s theory of evolution. This is the most useful and easiest machine learning algorithm.

In the genetic algorithm, a population of candidate solutions (called data set) is evolved towards better solutions. The evolution generally starts with the population (data set).

The algorithm takes it in the loop and after every loop, it shows the fitness function of each entity of data set. After a certain loops, the data set holds only those entities which are having high fitness function. These fitness functions show the probability of occurrence of an event.

To understand it in an easy way, let’s take an example. Suppose, you are running a store and you have a large data set, containing the information of all store items, their price, their selling, and users. So, when you load this data set into a genetic algorithm, it takes that data set into the loop.

After a certain loop, the algorithm shows the fittest data which implies the highest probability of events, such as, X number of people who have bought milk, bought baby diaper too, for Y number of times.

So now, next time, you please mind keeping the baby diapers and milk next to each other!

You can do the same thing in the mobile app. With a genetic algorithm, you can know the service and features which people like and the queries they have raised in a really comprehensive way.

If you push the boundaries of genetic algorithm (which is really easy), you can also predict the future demand and user behaviors.

2.  Naïve Bayes Classifier Algorithm

If you want to develop a news app or Pinterest like app in which classifying the images, documents, or any kind of screen content is required, the Naive Bayes Classifier algorithm comes to the rescue.

This is another most popular and useful machine learning algorithm that works on the popular Bays Theorem of probability for building machine learning models to classify contents.

Google has been using this on a large scale to classify the documents for indexing and page ranking.

3.  K Means Clustering Algorithm 

In the eCommerce app, the major feature of the app is the search button. This seems very tiny in scale, but it is very important as an eCommerce app owner can only manage to sell a satisfactory number of products if a user searches for Apple and the app shows him the iPhone and not a red apple!

But showing this kind of desired result isn’t possible without a machine learning algorithm.

K Means Clustering Algorithm is the machine learning algorithm for the cluster analysis. It classifies the data set with a certain number of clusters. So, now if a user searches for Apple, the clustering algorithm puts the iPhone into the Apple Product group and apple into the Apple Fruit group. This is how it manages to do that:

  • The online inventory an eCommerce online store contains is the data set.
  • When a user searches for Apple, the algorithm takes out all items related to that keyword from inventory or data set and puts them into a cluster.
  • Now, it takes two mean values from the selected cluster. Generally, the number of mean values is decided by an algorithm based on the number of product groups( Apple phone, Apple fruit).
  • The algorithm then puts the nearest values of each mean value into two different clusters.
  • After filling each cluster, it then takes an average of the values. This average number acts as the mean value for the next cluster.
  • The algorithm keeps doing this until it doesn’t get the clusters with the same mean values. When it gets the same mean values, the cluster is considered as organized – containing Apple iPhone details into the Phone cluster and Apple fruit details into Fruit cluster.

4.  Linear Regression machine learning algorithm 

In any kind of online or offline business, you must need to change the price with the change of external and internal factors. Sometimes, it makes you more profitable while sometimes, you mess it up.

So what about knowing the effects of price changes on demand and user experience before actually changing the price? It may sound like fantasy, but with the Linear Regression machine learning algorithm, you can make it happen.

Linear Regression machine learning algorithm basically shows the relationship between the 2 variables and what happens to another variable, if you change one.

This is very easy to use the algorithm and it runs very fast. Many big companies like Walmart and Target are deriving many business benefits with the Linear Regression algorithm.

5.  Matching Algorithm  

The current app development trend of developing an Uber-like app, e-scooter app and on-demand service providing app is not possible to accomplish without the matching algorithm.

The matching algorithm finds the nearest service provider or taxi driver or e-scooter and allocates it or him to the user. But how?

To understand it more rationally, let’s take an example of Uber which highly relies on matching algorithm.

As soon as a rider or user books the ride, the matching algorithm comes to action. It scans all nearby drivers and whoever is the nearest to the rider, is allocated for the ride.

However, this matching technique has one major drawback – many times, the nearest driver cannot reach the rider in minimum time because of obvious factors. So, why not solve this problem by taking that ‘obvious factors’ into account?!

Uber rewrote the algorithm and added features of the genetic algorithm in it with real-time outside data tracking.

Now, when a rider books the ride, the matching algorithm considers the result genetic algorithm gives and then also considers real-time outside data like traffic, one-way streets and weather to allocate the driver.

Meaning, it does not allocate the nearest driver, if a distant driver can make his way to the rider in less time.

Please note, you are always allowed to update the machine learning algorithms according to your requirements, like Uber updated matching algorithm!

Machine Learning the Conclusion: 

Machine Learning
Image developed by using Cana

Machine learning is the revolutionary technology, enabling app owners and app development companies to develop futuristic apps.

Machine learning algorithms make your users wonder how you know the same things they want. With the proper use of machine learning technology, you can offer the ultimate user experience and never witness a decreasing rate of app installment.

However, data scientists and mobile app development companies still have to figure out ways to bring deep learning into the mobile app industry and change the fate of it forever.

Author Bio:  Vishal Virani is a Founder and CEO of Coruscate Solutions, a leading taxi app development company.

He enjoys writing about the vital role of mobile apps for different industries, custom web development, and the latest technology trends.


Disclaimer:  This is a guest contribution to Philipscom Associates and the tips tricks, strategies, views, etc. mentioned in it does not reflect the view and opinions of P V Ariel or Philipscom blog. 

Opinions expressed by Philipscom contributors are their own. For More Information Read This


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  1. Liza Kosh
    Liza Kosh

    In the quest for more realistic worlds, captivating challenges, and unique content, video game development shops are increasingly turning to machine learning as a useful weapon in game development. Well said that major game development studios have teams researching, refining, and applying AI into their games.

    November 15, 2019
  2. Shawn Vipulaa
    Shawn Vipulaa

    In the journey for progressively sensible universes, enamoring difficulties, and novel substance, computer game advancement shops are progressively going to AI as a valuable weapon in game improvement.

    April 14, 2020
  3. Robin Rai
    Robin Rai

    magnificent article, article regarding such subject is of great help for those who are constantly looking knowledge to grasp on machine learning and it’s abilities.

    June 7, 2021

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