Python has a plethora of libraries and this is the reason it is a leading language in machine learning (ML). It is simultaneously simple and each library serves a unique purpose. The libraries cater to various aspects of ML including data manipulation and deep learning.
TensorFlow and PyTorch are two popular and powerful libraries with respect to deep learning. TensorFlow comes from the stable of Google and is basically known for its scalability. It is widely used in research and production environments.
PyTorch is the product of Facebook and is highly helpful to researchers for its dynamic computation graph. It allows flexibility in model building. The libraries have become staples to those who work on complex neural networks and deep learning projects.
Scikit-learn is a go-to library. It is basically for more traditional machine learning tasks. It offers a wide range of algorithms for tasks such as classification, regression and clustering. It is a versatile tool for many ML applications. It is simple and comprehensive. It is even helpful to those who are new to machine learning.
Keras is another popular library. It is designed to make development of deep learning models simple. It is built on top of TensorFlow and provides a user-friendly interface. It is capable in abstracting much of the complexity involved in creating neural networks. It is ideal for those who prioritize ease of use.
Apart from all these, Pandas and NumPy are fundamental libraries. These are basically for data manipulation and numerical computation. Pandas is good in handling and processing large datasets. NumPy provides support for large as well as multi-dimensional arrays and matrices. It also supports a collection of mathematical functions to operate on these arrays.
Matplotlib and Seaborn are simultaneously important for data visualization. These allow developers to create informative as well as aesthetically pleasing plots in analyzing and presenting findings.
