Python — Scikit learn and python libraries

What is Scikit learn?

Have you ever heard about Numpy, Pandas, Matplotlib, Scipy ?

These are free software machine learning libraries for the python programming language. Scikit is one among these libraries which is easy to use with few lines of code and can work with Numpy and Scipy.

Difference between these libraries

1. Numpy

It is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

It is basically used for complex math operations and functions.

2. Matplotlib

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy.

It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits.

Pyplot is a Matplotlib module which provides a MATLAB-like interface.Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.

It is majorly used for 2D and 3D plotting.

3.Pandas

Pandas is an open source data analysis and manipulation tool. It is fast, powerful, flexible and is easy to use.

4.Scipy

It is used to solve scientific and mathematical problems. It is built on the NumPy extension and allows the user to manipulate and visualize data with a wide range of high-level commands.

It is basically used for statistical and scientific calculations and domain toolbars.

5. Scikit

It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Why scikit?

The sklearn (scikit learn) library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Scikit-learn comes loaded with a lot of features:

1. Supervised learning algorithms
2. Unsupervised learning algorithms
3. Cross-validation
4. Feature extraction

and many more.

Applications of Scikit Learn

1. Used across all parts of the bank for classification.
2. Predictive analytics.
3. Music recommendations at Spotify etc.

And with that, this post comes to an end. I hope this post was helpful.