Scikit Learn A Brief Introduction

About Scikit-Learn 

• It is an open-source or free Machine Learning framework of the most popular language Python.
• It was started as a Google Summer of Code project by David Cournapeau in 2007 and later on its many versions has been released and the latest version is scikit-learn 0.21.3.
• It is very useful in data mining and data analysis and can be used for personal as well as commercial use.
• Works with many Python scientific libraries such as NumPy,SciPy, and Pandas.

Why  Scikit-Learn

Many machine learning frameworks are available for Python like Theano, SciPy, TensorFlow, PyTorch and many more but Scikit-learn is having some unique features that make it popular among the  Machine Learning developers community.

• Well Documented - The documentation is very vivid and its API is developer friendly.
  The API explanation is done in such a wonderful manner that one can easily integrate ones own app with it.

• BSD license - It has a BSD license; hence, there is a minimal restriction on the use and distribution of the software, making it free to use for everyone.
• Large Community Support - The community of Scikit-learn is very large and always active on the forum.Their community family is increasing day by day that makes it easy to use for beginners and get instant support. 
• Easy To Use - Since it is Pythonic library hence the world most easy programming language Python also enhances its popularity.
• Heavily used in the Industry  - Scikit-learn is used extensively by various organizations to predict consumer behavior, identify suspicious activities, and much more. Some of the technical giants are EverNote, AWeber, Yhat, Spotify etc.
• Machine Learning algorithms -  It covers almost all Machine Learning algorithms like SVM(Support Vector Machine),K-Neighbours, Random Forests etc.
• Algorithms flowchart - The most crucial thing in machine learning is to choose the right classifier for your dataset for model building and in this context Scikit-learn fits the best as it provides a flowchart that hepls to choose the appropriate classifier. Click here for flowchart

Where Not To Use

• It is not a deep/reinforcement package like Tensorflow and PyTorch.So, it will score poor in those cases.
• It does not provide Graphical Processing Unit support.
• It should not be used for data Visualisation as Matplotlib,Seaborn  etc. are used.
• For Natural language Processing(NLP),it should not be used.

Software prerequisites

• Python (2.7 or above)
• NumPy (1.6.1 or above)
• SciPy (0.9 or above)
• Scikit-learn

Installations process

• pip3 install numpy
• pip3 install scipy
• pip3 install scikit-learn

NOTE- use pip for Python version below 3 and use pip3 for Python version 3 or above.