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Machine Learning places immense power in the hands of its practitioners
Focus points include linear equations, logarithms, tensors, matrices and their multiplications and functions.
Probabilistic models and the theory of chance is considered to be a bedrock for statistics, which in turn is fundamental for machine learning.
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Combining probability with the good old mean, median, mode, variance, standard deviation etc is needed at the outset.
It is used scantily at the basic level; knowing gradients and partial derivatives allows you to make sense of backpropagation.
Machine learning has flourished mainly because of the capabilities provided by powerful programming languages like Python.
Trigonometry basics are specifically required for understanding a activation function called tanh in neural networks.
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