A Simple Introduction to Machine Learning for Beginners

ChatGPT, Midjourney, and Claude AI are taking the world by storm. Do you know what’s the common thing between all of these technologies? They are all based on the magic of Machine Learning! So, what is this “machine learning” concept? and how does it work? This article will answer all your burning questions.

What is Machine Learning?

Machine learning is a subfield of computer science and artificial intelligence (AI) that gives computers the ability to learn using data, without being specifically programmed. Machine learning algorithms train computers using data inputs, and do not explicitly use straightforward algorithms to directly manipulate data, as is the case in traditional computer science algorithms. Machine learning algorithms use inputs in the form of data to output values within a specific range of data. For example, facial recognition technology uses machine learning to give an approximate identity to each face based on similarities between the face and a previous collection of images of the same face in different situations. This also applies in the field of image search, that allows you to search for images similar to a certain image, or searching for something on the web using an image instead of text. Optical character recognition (OCR) also uses machine learning to transform text images into text. Recommendation engines use machine learning to tell users what movies or TV shows they can watch next, based on what they've already watched. In this article, we'll take a look at some of the most common machine learning methods and algorithms.

Machine Learning Using Supervised Learning

Supervised learning is a range of machine learning algorithms that learn from a set of training data that has been labeled with the correct answer. The algorithm first builds a model of the training data and then uses the model to predict the correct answer for new data. Supervised learning algorithms essentially compare their actual output with the ready and correct outputs to find errors, and modify the model accordingly. For example, a supervised learning algorithm may be given a large data set of images of children labeled as "child", and images of the sky labeled as "sky". Once the model is trained, using these labeled inputs, it can then correctly label new images of children and skies it has never seen before with a large degree of accuracy. Supervised learning algorithms can be used to learn complex patterns in historical data and to make