Differences between Machine Learning and Classical Programming
This article explains the key difference between Machine Learning and Classical Programming. Also, it highlights THREE different types of Machine Learning to give you some ideas and high-level understanding about ML.
Machine learning involves teaching a computer to recognise patterns by example, rather than programming it with specific rules. These patterns can be found in the data. In other words, ML is about creating algorithms (or a set of rules) that learn from complex functions (patterns) from data and make predictions on it (a form of “narrow AI”). ML learns from data and can be reused for unseen, future, or new data without rewriting code. Put another way, with ML, you start with a problem, identify data associated with that problem, use an algorithm to then model that problem, and generate output.
Classical Programming uses data and static, hard-coded rules to generate results whereas Machine Learning uses historical data or output, combined with new data, to generate new rules in a training model.
Three Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
Supervised learning is a method where a model learns from a data set containing input values and paired output values that you would like to predict.
Unsupervised Learning
Unsupervised learning is a method in which the training model learns from data without any guidance. The objective is pattern and structure recognition.
Reinforcement Learning
Reinforcement learning is a method in which the training model learns from its environment by being rewarded for correct moves and punished for incorrect moves.
Other reading: Machine Learning Basics
