Machine Learning Explained
Machine Learning is the process of letting your machine use the data to learn the relationship between predictor variables and the target variable. It is one of the first steps toward becoming a data scientist.
There are two kinds of Machine Learning: supervised, and unsupervised learning. In supervised learning, there are two types: Regression and Classification.
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A machine learning example
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3 Categories of Machine Learning
- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
- Unsupervised learning:
Polynomial regression
- Polynomials are algebraic expressions consisting of variables and coefficients.
- Polynomial regressions are the most used in machine learning.
Polynomial regression is a unique case of linear regression:
- You fit a polynomial equ...
Effective machine learning algorithms
Some of the most effective machine learning algorithms beyond deep learning include:
- ‘random forests’ that create multitudes of decision trees to optimise a prediction;
- ‘Bayesian networks’ that use a probabilistic approach to analyse variables ...
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