The Python ecosystem, growing at a rapid pace day by day, became the dominant platform for machine learning. Here we will discover the most useful components of the Python ecosystem for machine learning. Let’s get started.
SciPy, pronounced as “Sigh Pie”, is an ecosystem of Python open-source libraries for performing Mathematical, Scientific, and Engineering computations. SciPy is comprised of the following core packages relevant to machine learning −
NumPy − NumPy is a base N-dimensional array package for SciPy that allows us to efficiently work with data in arrays.
Matplotlib − Matplotlib is used to create comprehensive 2-D charts and plots from data.
Pandas − Pandas is an open-source Python package used to organize and analyze our data.
There are many ways to install SciPy, but followings are the two most popular ways −
You can also check how-to instructions for various platforms on the page https://www.tutorialspoint.com/scipy/scipy_environment_setup.htm
Scikit-learn (Sklearn) is the most useful and robust Python ecosystem for machine learning. Scikit-learn, built upon and requires SciPy ecosystem, provides us efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction. It is also open source and commercially usable under the BSD license. You can learn more about Scikit-learn at ttps://www.tutorialspoint.com/scikit_learn/index.htm
It is recommended to use the same methods to install Scikit-learn as you used install SciPy above. Apart from that, another easiest method to install Scikit-learn is to use conda. The command is given below −
conda install scikit-learn
One of the easiest ways to install the ecosystem is to use a distribution called Anaconda
. It includes Python, Scipy, and scikit-learn i.e., everything one needs to learn, practice, and use machine learning with Python environment.