Setting Up Python for Machine Learning on Windows – Real Python

Since 2011, Python has included pip, a package management system used to install and manage software packages written in Python. However, for numerical computations, there are several dependencies that are not written in Python, so the initial releases of pip could not solve the problem by themselves. To circumvent this problem, Continuum Analytics released Anaconda, a Python distribution focused on scientific applications and Conda, a package and environment management system, which is used by the Anaconda distribution. It's worth noticing that the more recent versions of pip can handle external dependencies using wheels, but, by using Anaconda, you'll be able to install critical libraries for data science more smoothly. Anaconda is a full distribution of the software in the PyData ecosystem, including Python itself along with binaries for several third-party open-source projects. Besides Anaconda, there's also Miniconda, which is a minimal Python distribution including basically Conda and its dependencies so that you can install only the packages you need, from scratch Conda is a package, dependency, and environment management system that could be installed without the Anaconda or Miniconda distribution.