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Getting started with machine learning requires some basic math knowledge as a prerequisite. Here are the steps you can follow:Linear Algebra : Linear Algebra is one of the most fundamental mathematical concepts in machine learning. You need to understand concepts such as vectors, matrices, determinants, eigenvalues, and eigenvectors. This knowledge is very important for understanding models and algorithms in machine learning.Calculus : Calculus is another fundamental mathematical concept in machine learning. You need to understand concepts such as derivatives, partial derivatives, gradients, and Taylor series. This knowledge is essential to understand the optimization algorithms and model training process in machine learning.Probability and Statistics : Probability and statistics are one of the cornerstones of machine learning. You need to understand concepts such as probability distribution, expectation, variance, hypothesis testing, confidence intervals, etc. This knowledge is very important for understanding probabilistic models and statistical inference in machine learning.Optimization theory : Optimization theory is one of the key concepts in machine learning. You need to understand concepts such as convex optimization, non-convex optimization, gradient descent, etc. This knowledge is essential to understand the optimization algorithms and model training process in machine learning.Deep Learning Theory : If you are interested in going deeper into the field of machine learning, you can learn more advanced mathematical concepts such as graph theory, information theory, functional analysis, etc. This knowledge is very helpful for understanding advanced machine learning algorithms such as deep learning.You can learn this math by self-study or by taking a course. Some excellent online courses, such as Coursera's "Machine Learning Basics" or "Deep Learning Specialization", provide great learning resources. In addition, you can also read relevant textbooks or academic papers to deepen your understanding of the mathematical foundations of machine learning. Good luck with your studies!
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