Machine Learning, evolving from deep learning, is a subset of Artificial Intelligence. Observing the patterns of human learning from textbooks, dictionaries as well as from experiences, data scientists explored and experimented with algorithms to imbibe the phenomenon of “learning” to machines.
Often involving in deriving logics (algorithms) that can learn from voluminous amounts of data, Machine Learning significantly improve with tasks. Using these algorithms, it allows computers to identify hidden patterns of data and structures the data according to the requirement.
Today, Machine Learning, mostly abbreviated as ML, is used almost everywhere: from self-driving cars, fraud detection to machine translations. In this process, app development, which revolutionized lives, included intelligent techniques like never before. At every step of app development, Machine Learning is used to enhance the development cycle.
Let’s see how ML is implemented in app development. Below are the top 10 ways:
This is the process of generating ideas for app development. Even though ideation might seem like an effortless process, an idea that works for app development is not easy. You will be amazed to know the number of ideas one can get just in a day.
Most of the ideas of app development are focused on solving a task or make the process simple. Machine Learning is used for market research, to acquire the current trends in the market, analyze them and define a mobile development strategy for any business in any sector.
2. Identify Your Users
Once you have formed your idea for app development, knowing your users is the key step. You may have a target group in your mind. However, until the idea matches with target group’s expectations, the purpose of an app is redundant.
Machine learning plays a vital in recognizing the compatibility of your app idea with different audience across the globe. And, it identifies several variables such as age, location, and demographics to make the next step in the development process
3. Data Mining Techniques for Requirement Analysis
Until you know what your users are expecting from your application, it is an impediment to app development. Data mining techniques are implemented to explore your business opportunities with the mobile application and derive a set of requirements to match your business goals.
Taking your target audience and your business model into consideration, the ML tools sort through facts to capture the most relevant demands of your customers. At the end of this process, you will mark your essentials in your app.
4. Pick a Color for Your Brand
The color of the app defines the brand in the market. You might be wondering, “Color and ML! How does color have anything to do with ML?” Surprisingly, color-coding your app is just more than a simple aesthetic factor.
It builds a perception on the users’ mind and distinct their experience from other millions of apps around the world. The advancements in the ML algorithms allows picking a color that best suits your business within seconds and add an additional advantage to your app. It relieves you from pondering hours to choose a color.
5. Feature Extraction
It is important that you select a reasonable set of features, “not too many and not too few”. But, how can you tell, what feature is important or what is not? Most often common sense helps.
But, how long can rely only on your common sense? There many techniques involved in this process, feature extraction. Selecting and combining the features becomes the part of app engineering. Data mining tools enable the systems to extract high-level features from low-level ones.
6. Interactive UI
With Machine Learning on the rise, conversational interfaces have come into place. Making it more user-friendly than ever, ML is enabling Graphic User Interfaces to simpler and removed the complexity.
There has been a significant usability improvement in UI with data mining techniques. For instance, a report of ONGO Framework says that “47 percent of the users actually look for map distance in a food delivery industry.” Based on such information, apps for different sectors are made accordingly.
Moreover, personalization to the individual level took the app UI to next level and allow every individual to have a different UI.
7. Face/Voice Recognition
Optical Character Recognition (OCR) and NLP techniques, native apps can use the mobile camera and voice sensors to build functions that allow voice and face recognition usage.
ML enables the systems to recognize faces, image segmentation, text and speech interpretation, spam detection and recognize motion.
8. In-built Robust Security Mechanism
Security is the primary concern of all the users. With ML, fraud detection is made easy. The identity recognition ML tools allow an app to build security schemas to prevent loss of data and security breaches.
ML learns from the user-behavior when there is a discrepancy in the behavior, it warns the users and builds security to the application.
9. App Development Frameworks
One of the fascinating outcomes of deep learning and machine learning is the evolution of app development frameworks. These frameworks designed with cognitive technologies allows the developers to remove the hassle of coding and build apps within minutes.
The drag and drop feature of these rapid app development frameworks make the application development faster, simpler and achieve desired results in less than the time it takes to plan. ML Frameworks simplifies different steps of app development and acquire a smart app.
10. Real-time Analytics and Updates
The machines constantly learn from the user-behavior. The identified patterns are can be acquired at any point. Even after development of the app, if the admin would like to change the app from the scratch, ML makes it possible.
ML tools provide a user-friendly dashboard for admin to make changes whenever it requires from color to UI.
Above points focused on Machine Learning in app development. Even though they may seem that it would take quite some time to get results, you can get data analysis with Machine Learning within seconds and reduce the development process up to 95 percent while reducing costs.
Moreover, the app becomes a self-learning system after development. From display to recommendations, ML plays a crucial role in defining the app usage. It only goes beyond imagination what Machine Learning (with Artificial Intelligence) apps can do in the later years.