How to Use Machine Learning in a Mobile App?

Machine learning is moving to an edge. It’s a widespread phrase you will hear everywhere, especially in the mobile app development industry. The use of machine learning in the mobile application is creating a buzz. The machine learning development services are in huge demand for creating innovative mobile apps. The trends for mobile app development constantly changes.

Machine learning is transforming the mobile app industry. From reducing the manual efforts to creating more intelligent apps, machine learning is doing exceptionally well to create innovation. It is helping businesses to connect with a broader audience and also help mobile app developers to deliver user-friendly applications. It offers the developer a faster way to create complex mobile apps.

The implementation of machine learning in mobile apps enhances the overall productivity and efficiency of the business. It also helps in improved understanding of the user’s point of view. Basically, ML leaves a major impact on all aspects of mobile app development. Let’s put a light on some key benefits of machine learning implementation in the mobile app:

Here we will take you through a few benefits associated with using machine learning in mobile apps:

  • Automates the Logic Development Process
  • Boost the Predictive Analytics within the Logic
  • Improving Search Capabilities and Advancing Results
  • Detecting Frauds Faster with Ease
  • Showcasing Relevant Ads to the Users
  • Virtual Assistants for Users

For the mobile apps that effectively leverage machine learning as a core component, it is a must to understand the features of machine learning. How to use ML? What are the possible use cases for future growth? Instead of wrapping everything in one go, let’s understand the key point to understand what machine learning can do inside a mobile application?

Computer Vision

With the latest tech innovation in both AI-accelerated chips and a smartphone camera, undoubtedly transformative use cases for machine learning on mobile applications rise in the form of computer vision. It allows computer systems to fetch and understand the content in videos and images. Computer vision-based machine learning belong to the following categories:

  • Understanding and interpreting content in video and images.
  • Tracking motion
  • Art and creativity

Image Recognition

Image recognition is a digital approach where computer systems categorize and interpret video and images. It is also labelled as “Image Labeling” and “Image Classification”. It helps in identifying vision-based machine learning problems and solving them. Image recognition is one of the profound computer vision tasks. The high-end functionality of image recognition can enable a transformative user experience. But, this innovation is not limited to

  • Automated image organization
  • User-generated content moderation
  • Enhanced visual search
  • Automated photo and video tagging
  • Interactive marketing/Creative campaigns

It helps in adding more uniqueness to mobile apps by quick interpretation of images and videos.

Object Detection

Object detection draws a box around the identified objects in an image or video. It locates the said objects in the entire scene, image, or video. People generally got confused between image recognition and object detection. Image recognition assigns a label to the image like an image of the coffee cup will be labelled as “coffee cup”. The image recognition gives a label to the identified image. Whereas object detection gives more detailed information in that particular image. It identifies a particular object in the scene.

Facial Recognition

Facial recognition is a smart approach to identify a person’s face from an image or video. Basically, this feature takes a lot of algorithms to recognize visuals to unique faces. There are different factors considered in facial recognition which are as follows:

  • Distance between the eyes
  • Nostril width
  • Nose length
  • Cheekbone height and shape
  • Chin width

Based on the above parameter it identifies a particular face. This feature is highly used in beauty apps.

Augmented Reality

Augmented Reality is not a feature of Machine learning. But, the combination of Augmented Reality and Machine Learning leads to an immersive and splendid user experience on the mobile app. There are already numerous mobile apps present in the market that have performed extremely well by implementing the combination of these two tech innovations AR and ML.

Speech Recognition

It is the computer-based automated processing of identifying a human voice. It allows mobile apps to respond by recognizing the human’s voice. Automatic speech recognition has created a buzz in the market in recent years. Entrepreneurs who are looking forward to creative mobile app development are asking to implement this feature to make it more user-friendly. Any mobile app development company is using this approach to deliver a consumer-oriented mobile app that delivers an interactive and engaging experience.

Conclusion:

The use of machine learning in mobile applications is not limited to the points we mentioned above. With the growing tech innovation and users’ demand, it helps entrepreneurs and companies to create advanced mobile apps. If you want to build a mobile app based on machine learning then you can hire a mobile app development company equipped with a team of tech professionals. Only an experienced Machine Learning Development Company can create a machine learning-based mobile app for you.

 

Author’s Bio: Brian Comel is an experienced mobile app developer at Sparx IT Solutions, a renowned mobile app development company. He is also fond of writing and sharing educational content with readers.

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