Top 7 Python Libraries for AI and ML

June 25, 2023 ebookreaders

Python is the go-to choice when developing Artificial intelligence projects. The programming language offers optimum security, performance, and scalability necessary for developing advanced artificial intelligence projects.

Additionally, developers across the globe love using Python as it comes with several AI-based libraries. As per Statista, Python for AI is trending and is used in 36% of Machine Learning based development projects.

Some of the top Python libraries for AI and ML are Numpy, Pandas, Matplotlib, SciPy, etc. The infographic below offers quick insights into the popularity of Python libraries for AI applications like Data Science.

Source

Here are the Top 7 Python Libraries for AI and ML

Summary

1. NumPy — Open-Source Numerical Library

Most AI developers view NumPy as the best Python library for developing a range of solutions. Developing AI with Python library NumPy lets you perform various mathematical operations on different matrices with ease. Additionally, it requires much less storage capacity and has excellent speed performance. You can use it to handle Fourier transformation, random numbers, sound waves, images, and various other binary functions.

Main Features of NumPy:

Arrays can be one-dimensional or multidimensional
Assists in implementing inter-platform functions
Comes with homogenous arrays
Offers array broadcasting
Multidimensional container for generic data

Real-Life Applications of NumPy:

Disaster Management
Mechanical Vibrations
Probability & Statistics

2. Pandas — Data Analysis Library

Pandas is another top Python library useful in handling Machine Learning solutions. The library offers assistance in analyzing and manipulating data and lets you manage the concept of multidimensional data, time series, etc. This open-source library can access and manipulate data from several sources like CSV, SQL databases, JSON files, and Excel. The advanced library comes with Series and DataFrames that assist in data representation and manipulation.

Main Features of Pandas:

Manage, explore, and manipulate data
Aligns and indexes data
Can handle missing values in the data
Supports multiple file formats
Performs mathematical operations on the data set

Real-Life Applications of Pandas:

Netflix recommendations
Stock prediction
Churn Rate in Banking

3. Matplotlib — Develops Interactive Visualizations in Python

This Python library helps with data visualization and is used to create plots, graphs, and charts. The availability of an extensive range of plots makes it ideal for use with different Python libraries, including Pandas, NumPy, SciPy, etc. It is open-source and can be used to build static and dynamic visualizations in the AI development project. If you know how to use MATLAB, you can easily use Matplotlib as it offers a similar user interface.

Main Features of Matplotlib:

Semantic way for developing subplot grids
Can set the aspect ratio of the axes box
Specifies the color of the legend text labels
Can directly use strings or functions as input for string formatters
Wrap your functions with custom decorators

Real-Life Applications of Matplotlib:

House Price Analysis
Credit Default Risk Analysis
Analyzing Climate

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4. SciPy — Embedded Modules for Array Optimization

SciPy(Scientific Python) is a NumPy-based open-source library. It is helpful in technical and scientific computations involving massive data sets. The library comes with high-level commands for data manipulation and visualization and works exceptionally well with images. You can use Scipy for image manipulation in the fields of scientific analysis and engineering. It provides you with embedded modules for array optimization, linear algebra, etc.

Main Features of SciPy:

Modules for optimization, linear algebra, integration, etc.
Applicable in signal and image processing
Wide range of mathematical functions
Special functions include Cubic Root, Exponential, Lambert, etc.

Real-Life Applications of SciPy:

Data Science Projects
Game Development
Scientific and Numeric Computing

5. Scikit-Learn — Supports Supervised & Unsupervised ML

Scikit-Learn was initially meant to be a third-party extension for the SciPy library. However, now it’s a full-fledged Python library with extensive clustering, regression, and classification algorithms. It is used mainly in conjunction with other libraries like NumPy and SciPy. Dedicated Python developers can manipulate this library for various purposes like analyzing customers’ behavior, predicting diseases, etc.

Main Features of Scikit-Learn:

Modules for optimization, linear algebra, integration, etc.
Applicable in signal and image processing
Wide range of mathematical functions
Special functions include Cubic Root, Exponential, Lambert, etc.

Real-Life Applications of Scikit-Learn:

Utilized by Spotify
Heart Disease Prediction
Analyzing Customer-Buying Behaviors

6. TensorFlow — Developed by Google

TensorFlow is one of the best Python libraries for AI and ML applications. The advanced library lets you leverage deep neural networks, natural language processing, partial differential equations, and more into the development project. Building unique responsive applications that offer optimum performance over desktops, PCs, smartphones, etc., is feasible with the TensorFlow AI library.

Main Features of TensorFlow:

Allows rapid and more effortless calculations
Supported by deep neural networks
Compatible with various computational platforms
Parallel Neural network training
Supports high-level APIs

Real-Life Applications of TensorFlow:

Speech/Image Recognition
Self-driving Cars
Sentiment Analysis

7. PyTorch — Applicable in NLP and Computer Vision

PyTorch is among the Python libraries for AI that gained immense popularity in much less time. Unlike most other AI libraries, it supports both GPU and CPU computations. PyTorch is helpful in developing computational graphs which are modifiable while running.

The library offers high-speed performance while handling simple, lightweight, or complex heavy graphs. Additionally, it has a machine learning compiler, Glow, for enhancing the performance of deep neural networks.

Main Features of PyTorch:

Seamless transition between modes
Dynamic Graph Computation
Automatic Differentiation
Tensors are generic n-dimensional arrays
Rich set of powerful APIs

Real-Life Applications of PyTorch:

Object detection
Generative application
Image classification Up

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Wrapping Up

That was all about the top Python libraries for AI development. Each library is suitable for building specific development projects. Based on your unique project needs, you can choose to create advanced functional features. Python development companies leverage these libraries for building up-to-date applications in less time.

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Top 7 Python Libraries for AI and ML was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.

https://ai.plainenglish.io/top-7-python-libraries-for-ai-and-ml-f0b4dad280ec?source=rss—-78d064101951—4
By: Emma Martin
Title: Top 7 Python Libraries for AI and ML
Sourced From: ai.plainenglish.io/top-7-python-libraries-for-ai-and-ml-f0b4dad280ec?source=rss—-78d064101951—4
Published Date: Wed, 21 Jun 2023 04:16:51 GMT