What is Machine Learning?
Also known as: ML
Machine learning is a branch of artificial intelligence in which algorithms learn patterns from data to make predictions or decisions, rather than following hand-coded rules for every situation.
It works by feeding large amounts of data into mathematical models that automatically adjust their internal parameters during training. The model improves its accuracy by minimizing errors on the training data and is then tested on new, unseen data.
Core approaches include supervised learning (using labeled examples), unsupervised learning (finding hidden structure in unlabeled data), and reinforcement learning (learning through trial-and-error rewards).
The overall workflow involves collecting and preparing data, choosing and training a model, evaluating its performance, and deploying it to solve real tasks.
Example
A spam filter learns to separate junk email from legitimate messages by analyzing millions of past emails that humans have already labeled as spam or not spam, then applies those learned patterns to new incoming mail.
Why it matters
Machine learning enables modern AI systems to handle complex, high-dimensional data such as images, speech, and text at scale, powering everyday technologies from recommendation engines to medical imaging.
Frequently asked questions
AI is the broad goal of building machines that can perform tasks requiring human-like intelligence; machine learning is one important technique for achieving AI by letting systems learn from data.
Related terms
Deep Learning is a subset of machine learning that uses multi-layered artificial neural networks to automatically learn complex patterns from large datasets.
Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.
A neural network, or artificial neural network (ANN), is a computational model inspired by the human brain that learns to recognize patterns in data by passing information through layers of interconnected artificial neurons.
Artificial Intelligence (AI) is the field of computer science focused on creating machines that can perform tasks typically requiring human intelligence, such as learning, reasoning, and decision-making.
Reinforcement Learning (RL) is a machine learning method where an agent learns to make sequential decisions by interacting with an environment, receiving rewards or penalties, and aiming to maximize its long-term reward.
Active learning is a machine learning technique where the model itself selects the most informative unlabeled data points to be labeled by a human, rather than labeling data randomly or all at once.