Top 10 Python Libraries for Machine Learning

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TOP 10 PYTHON LIBRARIES FOR MACHINE LEARNING

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With the increase in the markets for smart products, auto-pilot cars and other smart products, the ML industry is on a rise. Machine Learning is also one of the most prominent tools of cost-cutting in almost every sector of industry nowadays. ML libraries are available in many programming languages, but python being the most user-friendly and easy to manage language, and having a large developer community, is best suited for machine learning purposes and that's why many ML libraries are being written in Python. Also, the python works seamlessly with C and C++ and so, the already written libraries in C/C++ can be easily extended to Python. In this tutorial, we will be discussing the most useful and best machine-learning libraries in the Python programming language.

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TensorFlow : TensorFlow is a library developed by the Google Brain team for the primary purpose of Deep Learning and Neural Networks.

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Numpy: Numpy is a fast computational library that can handle tasks and functions ranging from basic algebra to even Fourier transforms, random simulations, and shape manipulations.

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Natural Language Toolkit (NLTK): The library can be further used for sentiment analysis, understanding movie reviews, food reviews, text-classifier, checking and censoring the vulgarised words from comments, text mining, and many other human language-related operations.

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Pandas : The Library is written in Python Web framwork and is used for data manipulation for numerical data and time series. It uses data frames and series to define three-dimensional and two-dimensional data respectively.

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Scikit-Learn: The library is written on the top of Numpy, Scipy, and matplotlib. It is an open-source and commercially usable library that is also very easy to understand.

Keras: Keras contains standard blocks of commonly used neural networks, and also the tools to make image and text processing faster and smoother.

PyTorch: The project is written in Python Web Development, C++, and CUDA languages. Along with Python, PyTorch has extensions in both C and C++ languages.

MlPack: MlPack is mostly C++-based ML library that has bindings to Python other languages including R programming, Julia, and GO.

OpenCV: OpenCV is an open-source platform dedicated to computer vision and image processing. This library has more than 2500 algorithms dedicated to computer vision and ML.

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10. Matplotlib: Matplotlib is a library used in Python for graphical representation to understand the data before moving it to dataprocessing and training it for Machine learning purposes.

Conclusion: In this blog, you learned about the best Python libraries for machine learning. Every library has its own positives and negatives. These aspects should be taken into account before selecting a library for the purpose of machine learning and the model’s accuracy should also be checked after training and testing the models so as to select the best model in the best library to do your task.

Source: https://www.zenesys.com/blog/top-10-python-libraries-for-machine-learning

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