Machine learning Libraries in python.

“If you want to teach people a new way of thinking, don’t bother trying to teach them. Instead, give them a tool, the use of which will lead to new ways of thinking.”
― Richard Buckminster Fuller

Machine learning is slowly slinking into society. The need to have the right tools to use when performing Machine learning will yield the expected results. This article will help you to learn the python libraries used in Machine learning.

Python language is a dominant tool used in ML withal, can’t be used alone requires support leading to the need for libraries that provide the necessary parts to work effectively in ML.
The reasons for using python are because of the nature of python language that is easy to learn, flexible and powerful, a community-based language, and a wide variety of libraries that make it easy to program complex problems.

Python libraries

Library, a collection of code integrated into Python used in the programming of complex problems. Python libraries, modules that provide access to system functionality such as file I/O, that would not be accessed by a programmer. These libraries make it easy to create code that solves complex problems.

ML libraries perform different tasks from data scrapping to the visualization of the data. Libraries are used during the process. Scrapy, used in web scrapping data is analyzed using pandas modeled using the sci-kit learn then visualized by the use of Seaborn or matplotlib libraries.

Here are the libraries that are common and most used in ML.With the libraries the journey in machine learning becomes a walk in the park.

Numpy

TensorFlow

Pandas

Keras

Sci-kit Learn

Matplotlib

Seaborn

PyTorch

Theano

Scrapy

Numpy

Numpy is an acronym for Numerical Python, This is a library for the Python programming language, used in adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Numpy offers multiple functions to perform mathematical operations.

TensorFlow

It is an open-source machine learning framework for developers created by google. It is used in the implementation of machine learning and deep learning applications. TensorFlow is designed in Python programming language, hence is an easy to understand framework.

Pandas

Pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool. It is the most common tool used by Data analyst Data scientists working with data and use the python platform.

It is used in the analysis and processing of data. [pandas] is derived from the term “panel data”, an econometrics term for data sets that include observations over multiple periods for the same individuals. — Wikipedia

Keras

It is an open-source, easy to learn neural network library written in python with the capability of running on top of Tensorflow, R or Theano. It’s designed with the focus of experimenting with deep learning techniques, such as creating layers for neural networks and maintaining the concepts of shapes and mathematical details.

Sci-kit Learn

Sci-kit-learn commonly known as (Sklearn). It is a useful and robust library for machine learning in Python. It provides a selection of tools for machine learning and statistical modelling by featuring various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, and k-means. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Matplotlib

It 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 like Tkinter and GTK.

It allows one to generate interactive data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots). Used in visualization of data relationships.

Seaborn

This is an open source Python library that provides a high level API data visualization it is based on matplotlib. It provides a high-level interface for drawing informative statistical graphics.

Pytorch

This is an open source machine learning library that is based on the torch library. It is used for applications such as computer vision and natural language processing.

Theano

It is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures.

Scrapy

This is a library that is categorized among the most used Data Science libraries.It is a library that is used in the scraping of data. It is a great tool for scraping data in Machine Learning models.

It can also be used to extract data using APIs or as a general-purpose web crawler

There are numerous python libraries out there, it is a matter of choosing the best combination of tools to work with. other libraries are Statsmodel, Requests, Scipy, LightGBM ,Eli5.

Summary

When Richard Buckminster Fuller said “If you want to teach people a new way of thinking, don’t bother trying to teach them. Instead, give them a tool, the use of which will lead to new ways of thinking.” he was certain that with tool you gain the experience and learn how to use the tools to your benefit. This is why python language also offers a variety of tool to use.

The combination of ML libraries that perform different tasks from data scrapping to the visualization of the data will lead to a successful data career. Scrapy, used in web scrapping data is analyzed using pandas modeled using the sci-kit learn then visualized by the use of Seaborn or matplotlib libraries.

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