Types of Self Learning
Explore the three main types of self-learning in machine learning: supervised learning with labeled data, unsupervised learning for pattern discovery without labels, and reinforcement learning for decision-making through trial and error. Understand how these methods analyze variables to create predictive models.
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Dependent and independent variables
As with other fields of statistical inquiry, machine learning is based on the cross-analysis of dependent and independent variables. The dependent variable (y) is the output you wish to predict and the independent variable (x) is an input that supposedly impacts the dependent variable (output).
Machine learning aims to find how the independent variable/s (x) affects the dependent variable (y).
For example: to predict the value of a house, a machine learning framework called “supervised learning” analyzes the relationship between house features (distance to the city, number of rooms, land size, etc.) as independent variables and the selling price of other ...