Machine learning is transforming the world, but for many, the technical details remain complex and opaque. This article aims to demystify machine learning by explaining the foundational concepts in simple terms. Whether you want to learn for your career or to better understand this revolutionary technology, understanding the fundamentals is key.

What is Machine Learning?

Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without being explicitly programmed. It focuses on training algorithms on data to make predictions, classifications or take actions. The "learning" comes from the algorithm's ability to improve its performance by identifying patterns in data.

Machine learning algorithms are described as learning a "mapping function" that maps input data to output variables. This learned function then allows the algorithm to predict output values for new input data. Popular techniques like supervised learning, unsupervised learning, reinforcement learning and deep learning power modern machine learning.

Supervised Learning

In supervised learning, algorithms are trained using labelled datasets, meaning input data is paired with the correct output values. Popular examples include classification for tasks like image recognition and regression for predicting continuous values like pricing. By learning from these examples, the model learns to map new unlabelled inputs to outputs.

Supervised learning problems can be further grouped into regression and classification tasks based on the type of outputs:

• Regression algorithms predict continuous numerical outputs. Example: Predicting housing prices based on size, location, etc.

• Classification algorithms predict discrete categorical outputs. Example: Classifying tumours as malignant or benign based on medical test results.

Common supervised learning algorithms include linear regression, logistic regression, neural networks and support vector machines.