It is the platform machine learning, easy to learn, easy to test provides all the capability that any machine learning platform should have, lot of algorithms like encoders - binary encoder, one hot encoder provides implementation for all supervised and un-supervised learning provides all the ability to validate the model we can integrate easilty with mat plotlib, pandas, numpy and for serialisers lot of specific example tutorials in internet available for the beginners It is open source and totally free lot of the other open source and many propriatry products for ml are developed on top of the sci kit library as it provides python interface easy to learn and integrate with many other platforms

Amazingly useful tool set for machine learning and data science work. Personally use it in python and it's really helpful. Some popular package such as pandas, numpy and matplotlib add it even more values. I always use it besides neural networks and yield solution as a combination, and the solution gives the best result often comes from it, by working on different points.

I like the fact that it includes a ton of functionalities and incorporates almost all of the Machine Learning algorithms meant for supervised and unsupervised learning. It can be used to develop various regression, classification and clustering algorithms. It utilizes a range of machine learning, preprocessing, cross-validation and visualization algorithms. It provides three Regression Metrics namely Mean Absolute Error, Mean Squared Error, R² Score. It also provides three Classification Metrics namely Accuracy Score, Classification Report, Confusion Matrix. Additionally, it provides three Clustering Metrics namely Adjusted rand Index, Homogeneity, V-measure.