Machine Learning
A subset of AI in which systems learn patterns from data to make predictions or decisions without explicit programming.
In plain English
Machine learning is a way for computers to learn from examples instead of being told exact rules. The more relevant data they see, the better they get at making predictions.
Technical definition
Machine learning is a subfield of artificial intelligence in which algorithms infer patterns from data to make predictions or decisions and improve with experience. It includes supervised, unsupervised, and reinforcement learning paradigms, optimized by minimizing a loss function over training data.
Business use case
Companies apply machine learning to forecast demand, detect anomalies, personalize recommendations, and score risk. By learning from historical data, these models automate decisions that improve accuracy and scale beyond what manual analysis allows.
Example
A streaming service trains a machine learning model on viewing history to recommend the shows each user is most likely to enjoy next.
Frequently asked questions
Machine learning is a branch of AI where computers learn from examples in data, improving their performance on a task without being explicitly programmed with fixed rules.
The three main types are supervised learning (learning from labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through reward and feedback).
Machine learning is a subset of artificial intelligence, and deep learning with neural networks is in turn a subset of machine learning.
Keep exploring
Artificial Intelligence
Artificial intelligence is the science of making computers do tasks that normally need human thinking, like understanding language or spotting patterns. It is the broad umbrella that covers many smaller fields.
Neural Network
A neural network is a computer model inspired by how brain cells connect. It learns by adjusting many tiny connections until it can recognize patterns, like telling cats from dogs in photos.
Fine-Tuning
Fine-tuning is taking a model that already knows a lot and giving it extra training on your own examples. This teaches it to do a specific job better, such as writing in your brand's voice.
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