One of the most important reasons Python is so valuable to use for machine learning is because of its huge collection of libraries for computational algorithms and visualization tools. In this article, I’ll introduce you to the most important Python libraries for machine learning that you need to know.
Most Useful Python Libraries for Machine Learning
TensorFlow is one of the most powerful Python libraries for machine learning when it comes to numerical computation. It is particularly well suited and well-refined for the tasks of large-scale machine learning computations. It was developed by the Google Brain team and powers many large-scale Google services, such as Google Cloud Speech, Google Photos, and Google Search.
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So what does TensorFlow offer? Here’s a summary:
- Its core is very similar to NumPy, but with GPU support.
- It supports distributed computing.
- It includes some sort of just-in-time compiler that allows it to optimize calculations for speed and memory usage. It works by extracting the computational graph from a Python function, then optimizing it and finally executing it efficiently.
- Calculation charts can be exported to a portable format, so you can train a TensorFlow model in one environment and run it in another.
When it comes to applying algorithms, Scikit-Learn is one of the most widely-used Python libraries for machine learning, incorporating a wide range of cutting edge machine learning algorithms for both supervised and unsupervised problems. on a medium scale.
Scikit-Learn aims to bring machine learning to non-specialists using high-level general-purpose language. The emphasis is on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is released under the Simplified BSD License, encouraging its use in academic and commercial environments.
NumPy, short for Numerical Python, is the basic package for scientific computing in Python. The majority of most Python libraries for machine learning are built on NumPy.
The features offered by Numpy:
- A fast and efficient multidimensional array object ndarray.
- Functions for performing element-by-element calculations with arrays or mathematical operations between arrays.
- Array-based dataset disk read and write tools.
- Linear algebra operations, Fourier transform and random number generation.
- Tools for integrating C, C ++ and Fortran code connection to Python.
Keras is a perfect choice if you are searching for python libraries for machine learning that you need to assemble a deep learning model quickly and easily. This library is intended for operational work with deep learning networks while being designed to be compact, modular and expandable.
The Functionality that Keras offers:
- Works great on CPU and GPU.
- Supports almost all neural network models that can be combined to create more complex models.
- The platform is written entirely in Python, that is, you can use standard debugging tools.
PyTorch is one of the best libraries for machine learning to work with neural networks, a longtime competitor to TensorFlow. Developed primarily by the Facebook AI group. PyTorch has been used as a deep learning framework in the creation of opposing generative networks.
So these were the most important libraries for machine learning. Of course, there are more libraries for machine learning that you must know like Scipy, NLTK, and many more. But these were the libraries that you just cannot miss to master machine learning.
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I hope you liked this article on the most important Python libraries for Machine Learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.
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