Why Machine Learning is Great for Mobile App Development
Machine learning is a technology that allows computers to learn from the existing data using certain algorithms. Although many associate Artificial Intelligence with automation, it is the machine learning technology that allows devices to operate on their own without any human intervention. There have been many developments in the machine learning algorithm to support app development.
Developers are leveraging ML and AI to create intelligent applications that engage the users and assists them. If you are looking to integrate machine learning into your mobile application, this guide will help you understand its practical applications.
Why Machine Learning is Great for Mobile App Development
What are the different types of machine learning algorithms for app development?
Machine Learning technologies rely on algorithms to enhance their operation and learn on
the go. The ML algorithm becomes more accurate when ML apps incorporate more qualified data. To learn the connections between new and existing data, machine learning mainly uses the following algorithms:
1. Supervised Learning: This type of learning involves the ML algorithm relying on
examples and associated information to find and make correct predictions.
2. Unsupervised Learning: This type of learning involves the ML algorithm does not
reply to any associated information and identifies data patterns on its own.
3. Reinforcement Learning: This type of learning involves the app developer training
the ML algorithm to make certain decisions based on the environment.
Steps in Developing a Machine Learning App:
To develop an ML application it is important to first train the algorithms so that it trains itself later based on the given data. These are the five steps in ML app development:
1. Gathering and Filtering the Data: Since Machine Learning technology relies and
functions based on data. The very first step of ML development is collecting data and
ensuring that there are no errors or missing values in the data. This data then needs
to be randomized so that there is no particular fixed order the application has to
2. Choose a Model: Each algorithm serves a different purpose in machine
learning, thus, it is important that you pick the right model which is trained in making
the right predictions or giving the correct answers.
3. Test the Model: It is important to test your model to understand how it will function in the real world. The developer needs to introduce unseen data for testing the model.
This will help in tuning the model.
4. Improve Model Training: After the testing phase, you need to check whether you
need to improve model training in any way as this will improve its accuracy.
5. Make Predictions: Use the data, which you did not use before, for making
predictions. Test the model with unused data to understand how the model would
perform in the real world.
Now that we have an understanding of machine learning algorithms and the steps involved in machine learning app development. Here is why machine learning is great for mobile app development:
1. Helps Improve User Experience: Today, audiences look for ease of use, simplicity,
and convenience in mobile applications. More and more users prefer using their
mobile apps as they are on-the-go and more relevant than websites. In such a
landscape, a mobile app leveraging machine learning technology will be able to
provide a meaningful experience to users. Machine learning technology studies all
types of user data and determines how to optimize their experience based on user
intent. Machine learning can also be used to track important UX metrics such as
location, session time, products used, and devices used.
2. Improves Search Outcomes: If a user searched for a specific product, service, or
information; your mobile app must provide the most relevant results without fail. If the
search itself is time-consuming and challenging, it will only frustrate the user and
lead to more uninstalls. Using Machine Learning tools will solve these issues and will
display the most relevant results to users. These tools go the extra mile and evaluate
the user’s past actions, search history, or other types of requests made by them on
3. Offers security: A major benefit of machine learning apps is that it improves security
and protects the user’s privacy. Every developer needs to ensure that the app’s data
is private not only because they need to meet the General Data Protection
Regulations (GDPR) but also to protect the credibility of the company. Machine
learning strengthens the identification and authentic processes in applications, which
can be a valuable feature of e-commerce applications wherein users provide
sensitive information including, Credit/Debit card numbers, and bank details.
4. Data Mining: Data mining is essentially the process of identifying connections and
patterns from large datasets. It involves gathering, storing, managing, and analyzing
the relevant data. It is one of the primary functions of machine learning and refines
the Machine Learning application to give more accurate results. Data mining can
comb through a lot of information and machine learning makes automatic
correlations between the information variables and incorporates these findings with
new algorithms. Machine learning apps keep getting better with newer algorithms
created by data mining unlike apps written in fixed algorithms.
5. Enhances Personalization: The more personalization your app offers, the more
credible your company becomes for customers. With the help of machine learning,
companies can identify users and segregate them based on their needs or
preferences. It monitors user behavior closely and observes their purchase patterns
to recommend new items to users. Netflix, which is one of the top streaming
applications, uses machine learning to send users movie and series
recommendations based on their past viewing patterns. Personalization is important
especially in e-commerce applications as it engages the users, motivates them to
buy more, and helps them find relevant products in less time.
Though there are still many mobile applications that have fixed algorithm developers are
now shifting to AI and ML-based applications. Machine learning applications are dynamic
and adapt well unlike traditional mobile applications. Users are always looking for a more
customized experience while using applications and Machine Learning algorithms to help developers satisfy the end-users. These ML algorithms when integrated with mobile
applications are a game-changer and will continue to create meaningful experiences for