Machine learning explained

Able to learn from data, machine learning algorithms can solve problems that are too complex to solve with conventional programming

What is machine learning?

Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data. Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience. Whereas a rule-based system will perform a task the same way every time (for better or worse), the performance of a machine learning system can be improved through training, by exposing the algorithm to more data.

Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm). Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting numeric answers, such as the number of widgets that will sell next month in your Manhattan store).

Unsupervised learning is further divided into clustering (finding groups of similar objects, such as running shoes, walking shoes, and dress shoes), association (finding common sequences of objects, such as coffee and cream), and dimensionality reduction (projection, feature selection, and feature extraction).

Applications of machine learning

We hear about applications of machine learning on a daily basis, although not all of them are unalloyed successes. Self-driving cars are a good example, where tasks range from simple and successful (parking assist and highway lane following) to complex and iffy (full vehicle control in urban settings, which has led to several deaths).

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