Machine learning explores the study and construction of algorithms that can learn from and perform predictive analysis on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning “signal” or “feedback” available to a learning system. These are
- Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
- Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
- Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal. Another example is learning to play a game by playing against an opponent.
In this chapter, DataCamp mainly introduces 3 ML tasks:
Each of them is applied to different categories of data. Classification is suitable for the data with predefined classes, it has qualitative output; this algorithm can be used in medical diagnosis, animal recognition etc. Regression model estimates its coefficients on previous input-output, its output is quantitative. Clustering is grouping objects in clusters, it’s similar with cluster and dissimilar between clusters; this algorithm identifies potential categories out of a group of observations, without knowing what the expected categories are in advance.
- GDJ, “a-i-ai-anatomy-2729794”, pixabay.com. [Online]. Available: https://pixabay.com/vectors/a-i-ai-anatomy-2729794/