 6 Math Foundation to Start Learning Machine Learning - Deepstash

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towardsdatascience.com

• Math enables you to select the right machine learning algorithm. It gives insight into how the model works, including selecting the right model parameter and validation strategies.
• Maths helps with creating the right confidence interval and uncertainty measur...

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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.

1. Linear Algebra
2. Analytic Geometry
3. Ma...

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97 Linear Algebra in machine learning is a systematic representation of data that computers can understand.

• It is the part of mathematics that uses vector space and matrices to represent linear equations.

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86 Analytic geometry is concerned with defining and representing geometrical shapes numerically. It extracts numerical information from the shapes numerical definitions and representations.

• Dist...

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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.

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Calculus is concerned with a perpetual change that consists of functions and limits. Vector calculus involves the differentiation and integration of the vector fields.

Useful terms:

• Derivative is a function of real numbers that m...

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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 a...

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86 Training a machine learning model is about finding a good set of parameters. The best value is the minimum value.

• Global minima: The point where a function best values takes the minimum value. When the aim is to minimise the function and solve it using optimisation alg...

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