Python is the go-to language for developers who need to apply statistical techniques or data analysis to their work. It has several predefined libraries & frameworks that help with machine learning (ML) and artificial intelligence (AI).
Libraries are sets of routines and functions that are written in a given language.
The extensive collection of Python libraries for machine learning simplifies development overhead and reduces development time.
Some of the most commonly used Python libraries in machine learning are:
Pandas is an open-source library that offers a wide range of tools for data manipulation & analysis. The library also allows us to read data from a range of formats such as CSV, Excel files, SQL databases, and JSON files.
Pandas comes with several built-in methods for combining data and grouping and filtering time-series functionality.
NumPy is a very popular Python library used for large multi-dimensional arrays and matrix manipulation. It is the fundamental package for scientific computing with Python and is mostly used for solving matrix problems.
The scikit-learn library provides a range of supervised and unsupervised learning algorithms. This library is mainly focused on model building, but it has a broad range of clustering, regression, and classification algorithms such as gradient boosting, random forests, k-means, etc. It also enables users to perform tasks like transforming data, feature selection, and ensemble methods.
Theano is a great Python machine learning library that has the ability to deal with computations in large neural networks. It aims to boost the development and execution time of ML apps, particularly those in deep learning algorithms. The only drawback of Theano is that its syntax can be quite difficult for beginners.
TensorFlow uses a system of multi-layered nodes that allow users to quickly set up, train, and deploy artificial neural networks with large datasets. It is used by Google to identify objects in photos or understand spoken words in its voice-recognition app.
TensorFlow is an open-source library for high-performance numerical computation that is widely used in the field of deep learning research and application.