Scikit-learn: This is a free and open-source library that provides a collection of efficient algorithms for various machine learning tasks, including classification, regression, clustering, dimensionality reduction, and model selection. It's known for its ease of use and being beginner-friendly.
TensorFlow: Developed by Google, TensorFlow is a powerful open-source library for numerical computation and large-scale machine learning. It offers a flexible architecture that allows you to deploy models on various platforms, from desktops to mobile devices.
PyTorch: Another popular open-source library for deep learning, PyTorch is known for its dynamic computational graph, which enables for more intuitive coding and debugging. It's particularly well-suited for research due to its flexibility and speed.
Keras: While not a standalone library, Keras is a high-level neural network API that can be used on top of TensorFlow or PlaidML. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
Fastai: Built on top of PyTorch, Fastai is a deep learning library that provides high-level abstractions to simplify complex tasks. It's known for its focus on ease of use and offers pre-trained models for various applications.
OpenCV (Open Source Computer Vision): This open-source library is a must-have for computer vision tasks. It offers a comprehensive set of algorithms and tools for image and video analysis, including feature detection, object tracking, and image recognition.
Transformers: This library from Hugging Face is specifically designed for natural language processing (NLP) tasks. It provides pre-trained models for various NLP applications, such as text classification, question answering, and machine translation.
cuML: Developed by NVIDIA, cuML is an open-source library for GPU-accelerated machine learning. It provides optimized implementations of popular machine learning algorithms, allowing you to train models significantly faster on NVIDIA GPUs.
cuML: Developed by NVIDIA, cuML is an open-source library for GPU-accelerated machine learning. It provides optimized implementations of popular machine learning algorithms, allowing you to train models significantly faster on NVIDIA GPUs.
PyCaret: This relatively new library aims to encapsulate machine learning workflows in a low-code manner. It automates many machine learning tasks, making it a good option for beginners or those who want to quickly build prototypes.