Top 5 Examples of Machine Learning app development

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It is quite surprising how our food delivery apps show (suggest) us restaurants serving the kind of food which we would like to order. Isn’t it also fascinating how we can track the real-time locations of our Uber rides? Do you know what drives this technology? Buckle up as you’re about to find out the answer.  

The facilitator is mobile machine learning or integration of machine learning in mobile apps 

Big tech companies use machine learning to create those interesting reactions in their mobile apps. In addition to the use of artificial intelligence in mobile applications, integrating machine learning is mainstream nowadays. But mobile machine learning is not a cakewalk. It is neither walking on eggshells.  If you want to learn how to integrate machine learning into your mobile applications, then you are at the right place. Your next few minutes will be spent on reading (learning): 

      • most common machine learning algorithms 
      • how to integrate machine learning into a mobile app development that is industry-specific 
      • best machine learning examples and how they work 

Before we move forward, let us take a glance at what machine learning is and why it should be integrated into mobile applications.  

What is Machine Learning?   

When we speak of the present, we are already talking about yesterday’s future. Our present and the upcoming future are defined by technology—which further drives machines. It is rather pensive to think how machines are an important part of our life. A machine has to be very sophisticated to learn on its own any behavioral patterns that we subconsciously follow. It is These machines not only imitate us but also follow our patterns quite precisely. The major driver behind this is machine learning.  

Machine learning is a branch or subset of artificial intelligence and computer science. It has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. ML focuses on the use of data and algorithms to imitate the way humans learn and gradually improves its accuracy.  

How is ML Beneficial? 

Following are the benefits of integrating machine learning: 

  • 76% of businesses saw an increase in sales after ML integration 
  • ML technology predicts better user behavior, optimizes processes, and leads up-sell and cross-sell 
  • 50% of companies are using machine learning to improve their marketing strategies 
  • ML helped several European banks increase their product sales by 10% 

Let us now focus on the various types of machine learning algorithms available for Android or iOS apps.  

 

“The more precise ML algorithms are made with more data, the better.” 

  

Machine Learning employs the following algorithms to build models that reveal connections: 

      • Supervised learning is when an algorithm learns by using example data and the associated target responses. These data could include numeric or string labels, such as classes and tags. ML can then predict the correct answer if presented with new examples. 
      • Unsupervised learning.ML learns by looking at examples and not having to look for answers. The algorithm thus determines data patterns by itself. 
      • Reinforcement Learning. Developers train ML algorithms so that they can make certain decisions from the environment. This allows the machine to capture the most accurate knowledge possible and make precise decisions. 

Use of Machine Learning in Specific Industries 

Machine learning has various applications. It can be used in different industries to create mobile apps. We have noted down some ML use cases in mobile apps that are industry-specific.  

1. AI-powered financial assistant 

Let us understand how ML is used for financing. You can use various mobile apps to gain insights into your finances. These apps are usually developed by banks to offer clients added value. They use machine learning algorithms to analyze transaction history, predict future spending, track spending patterns and provide financial advice to users. For instance, Erica is a mobile voice assistant developed by Bank of America. Over Erica’s financial assistant Erica, Currency offers more personal and convenient banking for 25 million mobile app users. 

2. Mobile fitness apps with ML  

Various workout apps, powered by machine learning, analyze data from smartwatches, wearables, and fitness trackers. Based on their user’s goals, they receive personalized lifestyle advice. To create customized fitness plans, the algorithm analyzes user’s current health and eating habits. One of the most popular fitness apps that use machine learning is Aptiva coach. It offers a variety of workouts and even custom Aptiva workouts. The app also tracks user progress. 

3. Healthcare mobile applications for healthcare with ML 

Many condition-based mobile apps make it easy to track heart diseases, diabetes, epilepsy, migraines, and other conditions. These apps use machine learning algorithms to analyze user input and predict possible conditions. They also notify doctors about current conditions for faster treatment. 

4. Transport mobile apps 

Mobile apps for logistics, such as Uber Trucking or Fleet Management, must provide drivers with current information on traffic conditions. These apps then optimize roads based on current conditions to avoid traffic jams and deliver cargo on time. Developers integrate machine learning algorithms with traffic prediction software into road optimization mobile applications to receive this traffic information before they happen. This algorithm analyzes historical traffic data and predicts traffic patterns for a specific day and time. Learn more about machine learning applications in transportation by reading the article How AI is changing logistics. 

5. E-commerce 

Machine learning algorithms can be used in a variety of ways by online retail mobile apps. These algorithms can be used to offer more relevant product recommendations to buyers based on their purchase history, credit card fraud identification, and visual search. You can find more machine learning applications in mobile eCommerce apps by reading the article on how online apparel retailers can leverage AI to sell online. 

  

5 Common Examples of Mobile Machine Learning Integration 

Innovative algorithms improve the user experience on their mobile devices and bring new machine learning mobile app ideas. Below is a list of the top machine learning apps. 

1. Snapchat 

This application uses machine-supervised learning algorithms for computer visualization. The algorithm for computer vision was developed by Looksery, a Ukrainian startup. This company was soon acquired by Snapchat for $150 million. The mobile machine learning algorithm uses photos to find faces and add fun elements such as glasses, hats, ears, and more. We have provided a detailed explanation of how ML Snapchat filters operate in this article. 

2. Yelp 

The app uses supervised machine learning to improve user experience by recommending “Recommended For You” collections. The ML algorithm reviews each restaurant. The ML algorithm then determines which dishes are most popular based on how often the meal has been mentioned. Yelp also uses ML to collect, classify and label user-submitted photographs of dishes with different attributes. These attributes include “ambiance is elegant” and “good with children” with 83% accuracy. 

3. Facebook 

Facebook uses machine learning algorithms in many ways. After the ml algorithm has analyzed your profile, interests, current friends, and their friends, Facebook suggests new friends to you in the “People You May Know”. The algorithm can also pull in other factors to suggest people you might know. Facebook also uses machine learning in Newsfeed, targeted ads, and facial recognition. 

4. Netflix 

Netflix uses machine learning algorithms. It has incorporated precise, personalized references by using linear regression and logistic regression along with other similar algorithms. Netflix’s mobile app uses a diverse range of content based on variety, actors, user and critics’ reviews, and much more for its audience. This information is studied by machine learning algorithms.  

In the case of Netflix, ML algorithms are trained by user actions that track users’ behavior. These algorithms study what TV shows are mostly watched by users and the type of reviews received online. These algorithms are familiar with user behaviors and hence offer exceedingly personalized content.  

5. Google Maps 

Interestingly, Google Maps also utilizes machine learning algorithms to gather and study data from a very large number of people. Researchers on Google ask questions like how long it takes for commuting or if they face any difficulty to find vehicle parking. They derive, aggregate, and use this information by creating various training models from people who have shared their location information.  

Final Thoughts: Machine Learning and Mobile Apps 

Machine learning algorithms can improve customer experience, loyalty, engagement, and similar aspects. It is very suitable for any mobile app that requires predictions and leverages enough data.  

Today, machine learning has numerous applications, from banking to healthcare. Depending on the needs of your business, you may be able to leverage any one of these ML algorithms. Last but not least, you need to hire an experienced team to develop machine learning apps. 

Anwesha Mishra

Anwesha has been a creative writer for a while. Currently, on her pursuit of tech writing, she is diving into the realms of technology to produce better content on the forever-changing world of technology. In her free time, you’ll find her humming tunes of her favourite shows or reading a book.

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