👉 Machine learning (ML) heavily relies on mathematical concepts from statistics, linear algebra, calculus, and optimization theory. At its core, ML involves using algorithms to learn patterns from data, which requires understanding probability distributions and statistical inference to make predictions or decisions. Linear algebra is crucial for representing data in high-dimensional spaces, performing operations like matrix multiplication and eigenvalue decomposition, and understanding neural network architectures. Calculus, particularly optimization techniques like gradient descent, is used to minimize loss functions and find the best model parameters. These mathematical tools enable ML models to generalize from training data to unseen examples, making them powerful for tasks like classification, regression, and clustering.
