6 Math Foundation to Start Learning Machine Learning
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Matrix decomposition is about how to reduce a matrix into its constituent parts. It tries to simplify complex matrix operations on the decomposed matrix instead of the original matrix.
There are many ways to decompose a matrix using a range of different techniques.
Training a machine learning model is about finding a good set of parameters. The best value is the minimum value.
Machine Learning is a tool that Data scientists use to obtain valuable patterns. Learning the math behind machine learning could give you an edge.
These six math subjects are the foundation for machine learning.
Calculus is concerned with a perpetual change that consists of functions and limits. Vector calculus involves the differentiation and integration of the vector fields.
Analytic geometry is concerned with defining and representing geometrical shapes numerically. It extracts numerical information from the shapes numerical definitions and representations.
Important terms that will help you start learning this subject:
Linear Algebra in machine learning is a systematic representation of data that computers can understand.
Probability is the study of randomness. Probability distribution is a function that measures the probability of a specific outcome associated with the random variable.
Probability theory and statistics are about different aspects of uncertainty.
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