For data science tasks, Python is the most common programming language. Machine learning, on the other hand, is a hot issue these days all across the world.
Python packages for machine learning have become the de facto language for developing machine learning algorithms. Python is required to understand data science and machine learning.
The top Python machine learning libraries to look into in 2022 are listed below.
TensorFlow
TensorFlow is an open-source numerical computing toolkit for neural network-based machine learning. It was developed in 2015 by the Google Brain research team for internal use in Google products.
Later on, it gained a lot of traction among numerous corporations and start-ups, like Airbnb, PayPal, Airbus, Twitter, and VSCO, which all use it in their tech stacks. It’s a great Python machine learning library to look at.
PyTorch
PyTorch is one of the most popular machine learning libraries, created by Facebook’s AI research team. It’s utilised for jobs like natural language processing, computer vision, and others.
It is one of the most used Python machine learning libraries. Microsoft, Facebook, Walmart, Uber, and others are among the companies that use it.
Keras
Keras is a rapid deep neural network experimentation platform that has recently obtained a separate Python machine learning library. It offers a robust machine learning toolkit that helps organisations like Square, Yelp, Uber, and others successfully handle text and image data.
It offers a user-friendly interface and can connect to multiple backends. Its architecture is flexible and adaptable. It’s a great Python machine learning library to look at.
Orage3
Orage3 is a software suite of machine learning, data mining, and data visualisation technologies. It was established in 1996 by scientists from the University of Ljubljana using C++. It’s a great Python machine learning library to look at.
Orange3 was chosen for this list because of its sophisticated prediction modelling and algorithm testing, widget-based structure, and ease of use.
NumPy
Python was not designed with numerical computing in mind. NumPy’s introduction was crucial in broadening Python’s capabilities as a set of mathematical functions on which machine learning solutions may be constructed.
Because of its robust computing capabilities, the broad programming community, and fast performance, this library is useful to use. It’s a great Python machine learning library to look at.
SciPy
This library, together with NumPy, is an essential tool for doing mathematical, engineering, and scientific computations. SciPy’s easy-to-use library, quick computational power, and enhanced computations are the major reasons Python experts like it.
SciPy is based on NumPy and can work with its arrays, resulting in superior quality and faster computational operations. It is one of the most used Python machine learning libraries.
Matplotlib
NumPy, Matplotlib, and SciPy are designed to work together to eliminate the requirement for the proprietary MATLAB statistical language.
Python packages are also free and more flexible, making them a popular choice among data scientists.
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