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The Backbone of Machine Learning
Linear algebra is a branch of mathematics that deals with vectors and matrices, forming the foundation for many Machine Learning algorithms.
Linear algebra enables efficient data manipulation and representation, allowing models to learn patterns and make predictions effectively.
Vectors represent data points Matrices can hold multiple data points
Techniques like Principal Component Analysis (PCA) use linear algebra to reduce data dimensions, preserving essential information while simplifying computations.
It relies on linear algebra to find the best-fit line by minimizing the least squares error between predicted and actual values.
It utilize matrix operations for forward and backward propagation, optimizing weights through gradient descent methods based on linear algebra.
Mastering linear algebra enables better understanding and implementation of Machine Learning concepts.