The term machine learning was coined by Arthur Samuel in 1959, but its potential to revolutionize mobile apps wasn't fully realized until Google launched PlayStore in 2008. With the advent of this era, app owners and development companies recognized the need to incorporate cutting-edge technology to meet growing user demands.

To achieve this, they turned to machine learning, a versatile technology that has been applied to various types of apps, yielding numerous benefits. Today, nearly all popular apps leverage machine learning to offer personalized user experiences and predict engagement rates.

However, machine learning is a complex concept that requires careful consideration of several parameters, including machine learning models, algorithms, and datasets. In this blog, we'll delve into the intricacies of developing machine learning-enabled mobile apps by exploring each parameter separately and providing a comprehensive example of using an algorithm and dataset to predict cyber attacks.

Before diving deeper, let's start with the basics: what is machine learning?

According to Wikipedia, "Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without human interaction." Breaking down this definition reveals two primary components: machine learning algorithms and statistical models. The latter can be considered as the business logic or code containing datasets along with the logic of the machine learning algorithm.

In essence, machine learning algorithms and datasets train the machine learning model to tackle real-world problems. A real-life example illustrates how machine learning works: supermarkets place diapers and milk next to each other because the machine learning model has found that people who buy diapers tend to also purchase milk.

The data from users and sales is stored as a dataset, which is then fed into a machine learning model consisting of an algorithm. The algorithm evaluates every attribute of the dataset and creates rules in the form of "if..then" statements (e.g., if a user buys diapers, then he will buy milk too).

Machine learning has numerous applications beyond this example. Its usefulness in mobile apps lies in its ability to provide personalized user experiences, chatbots, sales predictions, and cybersecurity.

Why AI Matters in Mobile Apps

  1. Personalized User Experience: Machine learning algorithms analyze user data to identify their interests, allowing for tailored content delivery.
  2. Chatbots: These AI-powered programs revolutionize customer service by providing instant responses and eliminating the need for human intervention.
  3. Sales Prediction: By analyzing historic and real-time data, machine learning models forecast sales, helping businesses make informed decisions.
  4. Cybersecurity: Machine learning modules can detect cyber attacks in real-time, alerting administrators or dropping malicious packages to prevent harm.

Top Apps Using AI

Some of the most popular apps leveraging machine learning include:

  1. Netflix: Uses Linear Regression and Logistic Regression to determine user interests and recommend relevant movies and shows.
  2. Snapchat: Applies machine learning to identify facial recognition patterns, enhancing its photo-sharing features.
  3. Instagram: Leverages machine learning for image recognition, allowing users to discover new content based on their interests.

By understanding the concepts and resources required to develop machine learning-enabled mobile apps, developers can unlock the full potential of AI in their own projects.