Introduction

As Artificial Intelligence (AI) gets more involved in human day-to-day tasks, more relevant terminology becomes more familiar. Yet, some of them remain ambiguous. This situation applies to Machine Learning (ML). A few examples include Siri, Alexa, and self-driving cars. Despite utilizing these features, the public remains largely unaware of the nature of ML and its underlying mechanisms. Understanding the nature of ML may be intimidating. However, it does not require a sophisticated technical background. As a powerful technology, ML foundations can be effectively grasped when introduced to their basics. The purpose of the current article is to introduce readers to fundamental knowledge leading to an understanding of the basics of ML. 

Definition of ML

ML refers to the branch of AI that enables computers to improve their performance by analyzing data collected and making decisions independently based on this analysis. This process proceeds with limited interference from human beings involved in the process, e.g., programmers or developers. Hence, ML models are capable of addressing a large amount of data independently and making predictions and decisions accordingly without having to be programmed for every single task specifically. The model improves automatically based on the data presented. When marking emails received as “spam”, for instance, ML models manage to detect undesired emails among all new emails received by learning which characteristics distinguish a “spam” email from others. 

Types of ML

There are three major types of ML, i.e., reinforcement learning, supervised learning, and unsupervised learning. Reinforcement learning is based on the concept of positive reinforcement in psychology. Hence, ML models are trained on making several decisions and getting rewarded for the correct ones. Usually, this type is used in developing games where AI models learn via trial and error. In supervised learning, however, ML models are trained on labelled data. This means that the data entered is accompanied by the correct output required. For instance, training ML models on recognizing images of oranges among several orange and non-orange images. Unsupervised learning is different. Here, ML models are trained on unlabelled data. Then, they detect patterns on their own, e.g., grouping images of food based on categories.

Importance of ML

Based on the information presented above, ML can be utilized to help computers solve complex problems more effectively than human beings. Repetitive automated tasks can be fulfilled easily using ML. Meanwhile, a large amount of data can be analyzed faster to make decisions in various fields professionally. ML has several applications in medicine, engineering, marketing, big data analysis, etc. For instance, in the realm of medical care, ML can be used to detect diseases in early stages to avoid deterioration. Furthermore, in finance, deceitful transactions can be identified more accurately. Today, online experiences are improved via the use of ML models, e.g., daily tasks, entertainment, online shopping, etc. Predictably, demand for ML models will increase as new industries are introduced to humanity. 

Conclusion

ML is the cornerstone of AI technology today. This necessitates seeking further understanding of its fundamentals. This article highlights ML as a core AI branch interfering with various industries and leading to making more accurate decisions as new data is provided.