These 10 python machine learning libraries are the best
Python is the most popular programming language for data science projects. And on the other side, machine learning is a trending topic that is across the globe these days. Python machine learning libraries have become the language for implementing machine learning algorithms. To grasp data science and machine learning, you need to learn Python. Here are the top Python machine learning libraries to explore in 2022.
TensorFlow is an open-source numerical computing library for machine learning based on neural networks. It was created by the Google Brain research team in 2015 to use internally in Google products. Later, it started to gain a lot of popularity among many companies and start-ups such as Airbnb, PayPal, Airbus, Twitter, and VSCO using it on their technology stacks. It is one of the top Python machine learning libraries to explore.
PyTorch is one of the largest machine learning libraries that was designed and developed by Facebook’s AI research group. It is used for natural language processing, computer vision, and other similar kinds of tasks. It is one of the top python machine learning libraries to explore. It is used by companies such as Microsoft, Facebook, Walmart, Uber, and others.
Keras is a fast experimentation platform with deep neural networks but it has soon gained a standalone Python ML library. It has a comprehensive ML toolset that aids companies such as Square, Yelp, Uber, and others to handle text and image data effectively. It has a user-friendly interface and has multi-backend support. It has a modular and extensible architecture. It is one of the top Python machine learning libraries to explore.
Orage3 is a software package that includes tools for machine learning, data mining, and data visualization. It was developed in 1996, the scientists at the University of Ljubljana created it with C++. It is one of the top Python machine learning libraries to explore. The features that make Orange3 qualify for this top list are powerful prediction modeling and algorithm testing, widget-based structure, and ease of learning.
Python wasn’t initially developed as a tool for numerical computing. The advent of NumPy was the key to expanding Python’s abilities as mathematical functions, based on which machine learning solutions would be built. Using this library is beneficial because of robust computing capabilities, the large programming community, and high performance. It is one of the top Python machine learning libraries to explore.
Along with NumPy, this library is a core tool for accomplishing mathematical, engineering computations, and scientific. The main reasons why Python specialists appreciate SciPy are its easy-to-use library, fast computational power, and improved computations. SciPy is built on top of NumPy and can operate on its arrays, ensuring higher quality and faster execution of computing operations. It is one of the top python machine learning libraries to explore.
Scikit-learn was firstly made as a third-party extension to the SciPy library. It is one of the top libraries on GitHub. The library is an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others. It is one of the top python machine learning libraries to explore. Scikit-learn also found a place on our list because it is great at classical machine learning algorithms, easily interoperable with other SciPy stack tools.
Pandas is a low-level Python library built upon NumPy. Everything started with the AQR financial company that needed help with quantitative analysis of its financial data. Wes McKinney is a developer at the company who started the creation of Pandas. Pandas have powerful data frames and flexible data handling. It is one of the top Python machine learning libraries to explore.
A unity of NumPy, Matplotlib, and SciPy is supposed to replace the need to use the proprietary MATLAB statistical language. Python packages are also available for free and more flexibly which can make a choice of many data scientists. It is one of the top Python machine learning libraries to explore. The reason to include Matplotlib is because of its comprehensive set of plotting tools.
In 2007, the ‘Montreal Institute of Learning Algorithms’ was created by Theano for evaluating and manipulating various mathematical expressions. Based on these expressions, the Python machine learning library allows building optimized deep learning neural networks. It has a stable simultaneous computing, fast execution speed, and optimized stability. It is one of the top python machine learning libraries to explore.
Share This Article
Do the sharing thingy