Python Libraries for Artificial Intelligence and Machine Learning

   Python Libraries for ML & AI

In this technological era python development services are increasing day by day.From Software development to web app development to game development and more, Python can be leveraged to create reliable digital solutions.

1.NumPy:

                  NumPy is short for "Numerical Python".It is a very popular library used for working with arrays.It also has functions for working in domain of linear algebra, Fourier transform and matrices.

It is open source project, and you can use it freely

pip install numpy

Key Features:

• Shape Manipulation
• Support for n-dimensional arrays
• Data cleaning and manipulation
• Random simulations

2.Pandas:

            Pandas is specifically use for Machine Learning concepts.It is basically written for the data manipulation and analysis.Pandas is best tool for handling this real-world messy data. And Pandas is one of the open-source python packages built on top of Numpy.Pandas make it easier for the developers to work with structured multidimensional data and time series concepts and produce robust results. 

Key Features

• Data reshaping and pivoting
• Merging and Joining of Datasets
• Data filteration
• Indexing of data
• Data alignment and handling of data 

3. Matplotlib

        It is data visualization library used for designing plots and graphs.It is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy.Developers can also use matplotlib's API to embed plots in GUI applications.

Key Features:

• High-Quality Diagrams 
• GUI Toolkit Support
• Map projections
• Recognition of data patterns

4.SciPy

        It is a leveraged by python development services to perform technical and scientific computing on large sets of data .It is particularly use for solving mathematical, scientific, engineering and technical problems.

Key Features:

• User-Friendliness
• Scientific and technical analysis
• Data visualization and manipulation
• Array manipulation subroutines

5.Scikit-learn:

                        It is a powerful python library that was originally supported to serve the purpose of data modeling and building machine learning algorithms.It is very widely used across all parts of the bank for classification, predictive analytics, and very many other machine learning tasks.                        

    

    following is an example to load iris dataset

Key Features:

• Classification of data
• Model selection
• Data modeling
• Pre-processing of data
• Dimensionality reduction

6.TensorFlow:

        It is an open-source ML library used for reaching data and production purposes.The library is offered by Google and can be used to make ML model building easier.TensorFlow offers a flexible framework and architecture that enables it to run on various computational platforms.However, it has its own tensor processing unit(TPU) through which is the best results can be obtained.

Tensorflow provides a collection of workflows to develop and train models using python and JavaScript and to easily deploy in the cloud, on-prem, in the browser or on the device no matter what language you use.

Key Features:

• Creating deep learning models
• Natural language processing
• Abstraction capabilities
• Managing deep neural networks
• Image, text and speech recognition