# Explain in detail Scikit-learning

Machine learning is a branch in computer science that studies the design of algorithms that can learn.

Typical tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions

Machine learning is a branch in computer science that studies the design of algorithms that can learn.

Typical tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions

It is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib!

The functionality that scikit-learn provides include:

**Regression**, including Linear and Logistic Regression**Classification**, including K-Nearest Neighbors**Clustering**, including K-Means and K-Means++**Model selection****Preprocessing**, including Min-Max Normalization

Machine learning is a branch in computer science that studies the design of algorithms that can learn.

Typical tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions

If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn.

In this post you will get an overview of the scikit-learn library and useful references of where you can learn more.

Where did it come from?

Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007.

Later Matthieu Brucher joined the project and started to use it as apart of his thesis work. In 2010 INRIA got involved and the first public release (v0.1 beta) was published in late January 2010.

The project now has more than 30 active contributors and has had paid sponsorship from INRIA, Google, Tinyclues and the Python Software Foundation.

Scikit-learn Homepage

Scikit-learn Homepage

What is scikit-learn?

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.