A Simple Introduction to Machine Learning

Machine learning (ML) is a complicated field. A “simple introduction” can seem like an impossible thing. In fact, it’s also a subfield of artificial intelligence (AI). The two have become closely entangled in recent years, though, so unweaving them can be a bit challenging.

If you ask experts what machine learning is, you’ll get several different answers. It’s a field and a science and a form of data analysis all in one. It’s the study and application of algorithms that operate within computer systems to carry out tasks using patterns, probability and insight. 

A simplified definition is still confusing, so breaking ML down is necessary to understand it. This introduction to machine learning will explain the past, present and future of the field. 

Where It Started

Machine learning is not new. It’s been around for about 60 years and has been evolving ever since.

In 1955, researcher John McCarthy created the term artificial intelligence in reference to the new, developing area of computer science. The goal during this period was to develop computers that could observe the world and turns those observations into the basis for making decisions. 

Four years later, in 1959, Arthur Samuel coined the term machine learning. It signified computers that could learn without overt programming. 

The two fields are directly related, but machine learning is a branch of AI. The two have become synonymous in recent years, but nevertheless have differences. ML allows a system to learn from data, whereas AI lets technology make decisions. 

Where It Is Now

Machine learning has come a long way from where it started. It shows up in many aspects of everyday lives and tasks. From laptops to the workplace, machine learning is everywhere you look.

Some of these machines may seem simple, but a significant amount of work goes into each process. 

ML requires three elements: data, variables and algorithms. 

The data is the base from which it learns and analyzes. Then it puts that information into context with variables like stock prices, recommendations or demographics. The algorithms help it solve and learn, evolving with each decision and result. 

This process provides you with things like product recommendations on Amazon or automated customer support. 

You may notice that ML involves a foundation of probability. After analyzing the base data, it tries to predict what to suggest or how to respond. For instance, if you just watched an action movie that stars a certain actor on Netflix, the predictive features may recommend a similar film with the same actor. 

As you provide feedback, ML learns your preferences and adapts. 

Classifications of ML learning also play a role in performance. They include supervised, unsupervised, reinforcement and semi-supervised. These kinds of learning speak to how much guidance or instruction the ML algorithms receive. 

A supervised learning algorithm receives data and derives rules or concepts from it. Unsupervised data entails the algorithm learning and forging its own path, reformatting information as it progresses. Reinforcement algorithms learn from feedback, both positive and negative. 

For example, if you were to reject that Netflix movie recommendation, the algorithm would relearn and reconfigure some of its processes. 

These innovations are evolving every day, but they are only the beginning of what’s possible.

Where It Will Go From Here

Machine learning is currently affecting the majority of technology. 

Speech and image recognition both feature ML algorithms. Siri is a good example of how it learns preferences and adapts to them. Decision making and reasoning are other key factors in ML and AI.

Experts and consumers have recently started to realize the field’s full potential. Some fear, though, that ML and AI are going to take over our lives, but other people assure that ML is changing the game and not necessarily pushing humans out.  

Regardless, ML has undeniably improved efficiency and simplified everyday tasks, paving the road toward an innovative way of life.