Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistence interface in Python.
Features of scikit learn are :
Supervised Learning algorithms
− Almost all the popular supervised learning algorithms, like Linear Regression, Support Vector Machine (SVM), Decision Tree etc., are the part of scikit-learn.
Unsupervised Learning algorithms
− On the other hand, it also has all the popular unsupervised learning algorithms from clustering, factor analysis, PCA (Principal Component Analysis) to unsupervised neural networks.
− This model is used for grouping unlabeled data.
− It is used for reducing the number of attributes in data which can be further used for summarisation, visualisation and feature selection.
− As name suggest, it is used for combining the predictions of multiple supervised models.
− It is used to extract the features from data to define the attributes in image and text data.
− It is used to identify useful attributes to create supervised models.
Some libraries apart from scikit learn are :