Key Features of NumPy for Machine Learning
- N-dimensional array (ndarray): A powerful object that allows for handling of large datasets efficiently.
- Mathematical and statistical functions: Includes operations like mean, median, standard deviation, and linear algebra functions for numerical analysis.
- Random number generation: Used in machine learning for initializing weights, bootstrapping, and creating synthetic data.
- Broadcasting: Allows operations on arrays of different sizes without the need for manual reshaping.
- File handling: NumPy allows reading and writing from CSV, binary, and other formats, making it easy to store and manipulate datasets.
- Memory efficiency: Uses less memory than Python lists due to optimized storage.