Useful Python libraries for AI and ML

Python libraries for AI an ML

Getting into Machine Learning and AI is not an easy task. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial to keep up with the pace of this rapid development. And Python has a large collection of libraries that used for Machine Learning and AI. Python is becoming popular day by day and has started to replace many popular languages in the industry.

So, the best Python libraries for Machine Learning and AI are -


Numpy is very much important for Machine Learning and Data Science. It's, of course, one of the greatest Mathematical and Scientific computing library for Python. Tensorflow and other platforms use Numpy internally for performing several operations on Tensors. One of the most important features of Numpy is its Array interface. The Array interface can be used to express images, sound waves or any other raw binary streams as arrays of real numbers with N dimensions.


PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.


Pymc is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems.


Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.


Neon is Nervana's Python-based deep learning library. It provides ease of use while delivering the highest performance.


Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modeling, classification, decoding, or connectivity analysis.


Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).


Chainer is a Python-based, standalone open source framework for deep learning models. Chainer provides a flexible, intuitive, and high-performance means of implementing a full range of deep learning models, including state-of-the-art models such as recurrent neural networks and variational auto-encoders.


Gensim is a free Python library with features such as scalable statistical semantics, analyze plain-text documents for semantic structure, retrieve semantically similar documents.


PyTorch, Tensors and Dynamic neural networks in Python with strong GPU acceleration.


Keras, a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It provides an easier way to express Neural networks. It also provides some of the utilities for processing datasets, compiling models, evaluating results, visualization of graphs and many more.


Another core library for scientific computing is SciPy. It is based on NumPy and therefore extends its capabilities. SciPy main data structure is again a multidimensional array, implemented by Numpy. The package contains tools that help with solving linear algebra, probability theory, integral calculus, and many more tasks. It faced major build improvements in the form of continuous integration into different operating systems, new functions and methods and, especially - the updated optimizers.


Pydot is a library for generating complex oriented and non-oriented graphs. It is an interface to Graphviz, written in pure Python. With its help, it is possible to show the structure of graphs, which are very often needed when building neural networks and decision trees based algorithms.


Pandas is a Python library that provides high-level data structures and a vast variety of tools for analysis. The great feature of this package is the ability to translate rather complex operations with data into one or two commands. Pandas contain many built-in methods for grouping, filtering, and combining data, as well as the time-series functionality.


This Python module based on NumPy and SciPy is one of the best libraries for working with data. It provides algorithms for many standard machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, and model selection.


Another Python Library that is tailored for the generation of simple and powerful visualizations with ease is Matplotlib. It is a top-notch piece of software which is making Python (with some help of NumPy, SciPy, and Pandas) a cognizant competitor to such scientific tools as MatLab or Mathematica.


Scrapy is a library for making crawling programs, also known as spider bots, for retrieval of the structured data, such as contact info or URLs, from the web. It is open-source and written in Python. It was originally designed strictly for scraping, as its name indicate, but it has evolved in the full-fledged framework with the ability to gather data from APIs and act as general-purpose crawlers.


These libraries are just a small sample of the tools available to Python developers. There are many other libraries and frameworks that are also worthy and deserve proper attention for particular tasks. So, if you have another useful library in mind, please let us know in the comments section. Thanks for your attention.

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