Best Python Libraries for Machine Learning| Artificial Intelligence #shorts #shortvideo #robofied

Best Python Libraries for Machine Learning:

NumPy- NumPy allows you to handle enormous multi-dimensional arrays and matrices using a huge number of high-level mathematical functions. It's especially handy for linear algebra, the Fourier transform, and random number functions

SciPy- It includes modules for optimization, linear algebra, integration, and statistics. SciPy may also be used to manipulate images.

Keras- Keras makes it possible for ML newcomers to create and construct a Neural Network. One of the finest features of Keras is that it enables for quick and easy prototyping.

PyTorch - It provides a large selection of tools and libraries that assist Computer Vision, Natural Language Processing (NLP), and many other ML projects.

Scikit-learn- It provides a diverse set of supervised and unsupervised learning algorithms through a consistent Python interface. Classification, regression, clustering, dimensionality reduction, model selection, and preprocessing are the primary machine learning functions supported by the Scikit-learn toolkit.

TensorFlow- It can train and execute deep neural networks, which may be utilised to create a variety of AI applications. TensorFlow is frequently used in deep learning research and development.

Pandas- Pandas was designed primarily for data extraction and preprocessing. It provides high-level data structures as well as a comprehensive range of data analysis capabilities. It has a plethora of built-in ways for searching, merging, and filtering data.

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