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  /  Latest News   /  Top Interesting Python Libraries for Machine Learning in 2021
Python libraries

Top Interesting Python Libraries for Machine Learning in 2021

Global Tech Outlook features the top interesting Python libraries for machine learning in 2021

Python is one of the popular and trending programming languages in the major cutting-edge technologies like artificial intelligence, machine learning, data science and many more. There are numerous Python libraries for machine learning available on the internet for convenient coding purposes. Companies prefer candidates who have sufficient knowledge and experience in one of these Python libraries for machine learning. Let’s explore some of the top Python libraries for machine learning in 2021 to drive revenue in a company efficiently and effectively.

 

Top interesting Python libraries for machine learning in 2021

TensorFlow

TensorFlow is known for its end-to-end open-source platform for machine learning with comprehensive and flexible tools, libraries, and a community. The platform helps in building and deploying machine learning models while solving real-life problems to overcome challenges in the business. It offers various levels of abstraction to choose the right one by using the high-level Keras API. The eager execution enables immediate iteration and intuitive debugging to provide a direct path to production. TensorFlow Extended helps in the full production of a machine learning pipeline and provides Ragged Tensors, TensorFlow Probability, Tensor2Tensor, TensorFlow Recommenders, Lattice, TensorFlow Federated, TensorFlow Privacy, BERT, and many more for Python in machine learning.

 

Scikit-learn

Scikit-learn is popular for providing efficient tools for predictive data analysis to be accessible to everyone. It is built on NumPy, SciPy, and Matplotlib as an open-source platform. It helps developers in classification, regression, clustering, dimensionality reduction, model selection and pre-processing. The Python library is one of the well-designed libraries for machine learning with advanced analysis in Python and supports leading-edge basic research.

 

PyTorch

PyTorchis an open-source machine learning framework to accelerate the journey from research prototyping to production deployment. It isan end-to-end platform that enables fast and efficient production through a user-friendly front-end and distributed training. It provides a seamless transition to graph mode for speed, optimization and functionality in Python. It offers services like torchaudio, torchtext, torchvision, TorchElastic, TorchServe and PyTorch on XLA devices. There are also platforms and libraries known as determined, PyTorch geometric, TorchDrift, TorchIO, transformers, captum, fastai, hummingbird and many more.

 

Pandas

Pandas is known as one of the top Python libraries for machine learning and an open-source software library written for this particular programming language for data manipulation and analysis. It helps in intelligent data alignment and integrated handling of missing data as well as intelligent label-based slicing, indexing and sub setting of large datasets. Python with Pandas is utilized in a wide array of academic and commercial fields such as neuroscience, statistics, web analytics and many more. The main aim is to be the fundamental high-level building block for a practical world of data analysis in Python.

 

NumPy

NumPy is known for supporting large and multi-dimensional matrices with high-level mathematical functions as a Python library for machine learning. It helps to bring the computational power of languages such as C to Python and offers the fundamental packages for scientific computing with Python. The NumPy vectorization, indexing as well as broadcasting concepts become the defacto standards of array computing. There are numerous mathematical functions, random number generators, linear algebra routines and so on with a wide range of hardware and computing platforms.

 

SciPy

SciPy is well-known as the open-source Python library for machine learning that can be used for scientific computing and technical computing. It contains modules for optimization, integration, interpolation, and many more in a SciPy ecosystem. The SciPy library contains a collection of user-friendly numerical algorithms and domain-specific toolboxes.