Understanding Machine Learning in 5 Minutes

Modern technology has evolved far beyond simple chatbots that provide automatic responses to different inquiries. Two of the most significant fields where technology is already disrupting and has immense potential are Machine Learning (ML) and Artificial Intelligence (AI). For instance, ML is being widely applied in the meditation and mindfulness industry to analyze human brain behavior in-app and generate relevant context-specific recommendations.

Image credit: Lina Leusenko

Basic Machine Learning Use Cases in Meditation

Enabling a smart search function for your app or website. Making various real-time measurements. Processing in-app user behavior and biometric indicators (such as heart rate and strains) to make personalized course/class recommendations.

My team has recently conducted research on this topic, and you can delve deeper into it by checking out our blog post titled "Machine Learning and Artificial Intelligence in the Meditation Industry" on Stromotion’s blog.

What is Machine Learning?

Machine learning is a subset of artificial intelligence within the field of computer science that often uses statistical techniques to enable computers to learn how to predict desired outcomes based on data.

How to Train a System for Machine Learning

The training of a machine learning system involves using mathematical techniques. This includes:

Algebra Statistics Logic Signal Processing

Learning these mathematical skills is essential as they form the backbone of understanding and implementing machine learning algorithms.

Types of Machine Learning Algorithms

Supervised Learning: This type of ML involves learning a function that maps an input to an output based on example input-output pairs. It relies on labeled training data consisting of a set of training examples. Unsupervised Learning: This involves drawing inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is clustering analysis, used for exploratory data analysis to find hidden patterns or groupings. Reinforcement Learning: This is an area of ML inspired by behaviorist psychology, focusing on how software agents should take actions in an environment to maximize a notion of cumulative reward.

Within 5 minutes, you now have a grasp of the fundamental concepts of machine learning. You know what it is, what types of algorithms it encompasses, and what skills are necessary to get started with learning about machine learning.

Related Keywords: Machine Learning, Artificial Intelligence, Supervised Learning