👉 Machine learning, at its core, is a subset of artificial intelligence that involves algorithms and statistical models to enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. The "math" in machine learning is fundamental and includes various branches such as linear algebra, calculus, probability theory, and optimization techniques. Linear algebra is used to manipulate and transform data through matrices and vectors, while calculus helps in understanding how functions change and optimizing them. Probability theory is essential for modeling uncertainty and making predictions, especially in scenarios with incomplete or noisy data. Optimization algorithms, like gradient descent, are employed to minimize error functions and improve model accuracy. Together, these mathematical tools allow machine learning models to process complex data, uncover patterns, and make accurate predictions or classifications.
