Since its introduction in 1991, Python has become one of the most popular programming languages. 

One of its best qualities is the user-accessible open-source libraries, which are practical and straightforward to understand. 

The libraries give users a framework for creating new machine learning (ML) models.

Machine learning offers several options for job advancement, whether you are currently familiar with it or wish to learn more. 

Indeed even named machine learning engineer as the most extraordinary career of 2019 due to the industry's 344 per cent job growth between 2015 and 2018.

What is a python Library?

A Python library is a collection of functions and classes that provides additional functionality to a Python program. 

Libraries are often distributed as standalone packages that can be imported into a Python program using the import statement.

Python libraries can provide a wide range of functionality, including support for numerical computing, data manipulation, machine learning, scientific computing, web development, and more. Some popular Python libraries include NumPy, Pandas, Matplotlib, and sci-kit-learn.

Python libraries are usually distributed as Python packages, which are collections of Python modules (Python files) bundled together. 

Packages are typically stored on the Python Package Index (PyPI), a repository of Python packages that can be easily installed using the pip package manager.

Using libraries is a common way to extend a Python program's functionality and avoid reinventing the wheel by writing code that has already been written and tested by others.

Here are nine Python libraries commonly used for machine-learning tasks:

NumPy

A library for working with large, multi-dimensional arrays and matrices of numerical data.

NumPy is a Python library for working with large, multi-dimensional arrays and matrices of numerical data. It provides various functions and data types for working with numerical data efficiently and effectively.

NumPy arrays are similar to lists in Python, but they are more efficient for certain types of operations and are more flexible in terms of the kinds of data they can hold. 

NumPy arrays can hold elements of any type, but they are most efficient when used with numerical data, such as integers and floating-point values.

NumPy provides various functions for performing operations on arrays, including mathematical, statistical, and linear algebra operations. 

It also includes functions for working with arrays, such as reshaping, slicing, and merging arrays.

NumPy is a powerful tool for numerical computing in Python, and it is widely used in scientific computing, data analysis, and machine learning. 

It is an essential library for anyone working with numerical data in Python.

Pandas

A library for organising and manipulating data in a tabular format.

Pandas is a Python library for working with tabular data. It provides data structures for efficiently storing and manipulating large amounts of data and tools for reading and writing data to and from various file formats and databases.

The primary data structure in Pandas is the DataFrame, a two-dimensional data table with rows and columns. 

Each column in a DataFrame can have a different data type, and the rows are labelled with an index.

Pandas provide a wide range of functions for manipulating and analysing data stored in DataFrames, including procedures for filtering, grouping, and aggregating data, handling missing data, and performing time series analysis.

Pandas is a popular library for data analysis and manipulation in Python, and it is widely used in fields such as finance, economics, and marketing. It is an essential library for anyone working with tabular data in Python.

Matplotlib

A library for creating static, animated, and interactive visualisations in Python.

Matplotlib is a Python library for creating static, animated, and interactive visualisations in Python. 

It provides various functions for creating different plots and charts, including line plots, scatter plots, bar plots, histograms, pie charts, and more.

Matplotlib is designed to be easy to use and provides a high-level interface for creating plots with a few lines of code. 

It also provides a low-level interface for fine-grained control over the appearance of plots, including options for customising the colours, fonts, and layout of fields.

Matplotlib is a powerful tool for data visualisation in Python, and it is widely used in scientific computing, data analysis, and machine learning. 

It is an essential library for anyone working with data in Python who needs to create visualisations to communicate their findings.

Seaborn

A library for creating statistical graphics and visualisations in Python.

Seaborn is a Python library for creating statistical graphics and visualisations in Python. It is built on top of Matplotlib and provides a higher-level interface for creating plots and charts with a more attractive and modern default style.

Seaborn is designed to be easy to use and provides various functions for creating different plots and charts, including line plots, scatter plots, bar plots, histograms, and more. 

It also provides functions for plotting statistical models and visualising the relationships between different variables.

Seaborn is a valuable library for data visualisation in Python, particularly for exploring and visualising statistical relationships in datasets. 

It is widely used in data analysis and machine learning and is an excellent tool for communicating findings to a broader audience.

Scikit-learn

A library for machine learning tasks, including classification, regression, clustering, and dimensionality reduction.

Scikit-learn is a Python library for machine learning tasks, including classification, regression, clustering, and dimensionality reduction. 

It is built on top of NumPy and SciPy, providing a simple and efficient interface for working with various machine learning algorithms.

Scikit-learn is designed to be easy to use and provides a consistent interface for working with different models. 

It includes functions for loading and preprocessing data and tasks for model training, evaluation, and prediction.

Scikit-learn is a popular library for machine learning in Python and is widely used in industry and academia. 

It is an essential library for anyone working with machine learning in Python, and it is particularly well-suited for tasks such as classification, regression, and clustering.

TensorFlow

An open-source machine learning framework for training and deploying machine learning models.

TensorFlow is an open-source machine-learning framework for training and deploying machine-learning models. 

It was developed by Google and is widely used in industry and academia for a wide range of machine-learning tasks.

TensorFlow is designed to be flexible and scalable, and it can be used for a wide range of machine learning tasks, including deep learning, reinforcement learning, and more. 

It provides various tools and libraries for building and training machine learning models and functions for deploying models in production environments.

TensorFlow is a powerful and widely-used machine learning framework, and it is particularly well-suited for tasks such as image classification, natural language processing, and speech recognition. It is an essential tool for anyone working with machine learning in Python.

Keras

A high-level API for building and training deep learning models using TensorFlow or other backends.

Keras is a high-level API for building and training deep learning models using TensorFlow or other backends. It is designed to be easy to use and provides a consistent interface for working with different deep-learning models.

Keras allows users to build and train deep learning models using a few lines of code and provides a wide range of functions for defining and optimising models. 

It also includes functions for loading and preprocessing data and for model evaluation and prediction.

Keras is a popular library for deep learning in Python and is widely used in industry and academia. 

It is an essential library for anyone working with deep learning in Python. It is particularly well-suited for tasks such as image classification, natural language processing, and speech recognition.

PyTorch

An open-source machine learning library for training and deploying deep learning models.

PyTorch is an open-source machine learning library for training and deploying deep learning models. 

It was developed by Facebook and is widely used in industry and academia for many machine-learning tasks.

PyTorch is designed to be flexible and scalable, providing various tools and libraries for building and training deep learning models. 

It also includes functions for deploying models in production environments and performing everyday deep-learning tasks such as image classification and natural language processing.

PyTorch is a powerful and widely-used machine learning library, and it is particularly well-suited for tasks such as image classification, natural language processing, and speech recognition. It is an essential tool for anyone working with deep learning in Python.

XGBoost

An optimised distributed gradient boosting library designed to be highly efficient, flexible, and portable.

XGBoost is an optimised distributed gradient boosting library designed to be highly efficient, flexible, and portable. 

It is widely used in industry and academia for various machine-learning tasks, including classification, regression, and ranking.

XGBoost is designed to be fast and scalable, providing various tools and libraries for building and training gradient-boosting models. 

It also includes functions for deploying models in production environments and performing everyday machine-learning tasks such as model selection and hyperparameter tuning.

XGBoost is a powerful and widely-used machine learning library, and it is particularly well-suited for tasks such as classification and regression. It is an essential tool for anyone working with gradient boosting in Python.

These libraries are all powerful tools for machine learning in Python, and they can be used together or independently to solve a wide range of machine-learning tasks.