scikit learn

An active library, scikit-learn is used in Python for the purpose of creating as well as solving advanced machine learning programming. A huge number of algorithms such as Regression, Model Selection, Clustering, Preprocessing, Classification and Dimensionality reduction including random forests, support vector machines, DBSCAN, gradient boosting and k-means are designed to interoperate with the scientific and numerical libraries of Python: SciPy and NumPy. At Digital Aptech, we have a team of dexterous Python developers who know the proper use of scikit-learn.

Machine learning in Python

It is through a consistent interface in Python that scikit-learn provides a wide range of unsupervised and supervised learning algorithms. Distributed under many Linux distributions, scikit-learn encourages both commercial and academic use. Built upon the SciPy (Scientific Python), the library must be installed before using scikit-learn. This stack includes:

• Matplotlib: Comprehensive 2D/3D plotting
• Sympy: Symbolic mathematics
• SciPy: Fundamental library for scientific computing
• NumPy: Base n-dimensional array package
• Pandas: Data structures and analysis
• IPython: Enhanced interactive console

The modules or extensions for SciPy care are known as SciKits. As the module provides learning algorithms, it is named as scikit-learn.