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Integrating python libraries

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Scikit-learn: This is a popular library for classical machine learning algorithms, supporting tasks like classification, regression, clustering, and more. It integrates well with other libraries like NumPy and Pandas. Scikit Documentation Tutorial
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TensorFlow is a comprehensive library for building and deploying deep learning models. It is widely used for large-scale machine learning and neural network projects. (https://www.tensorflow.org/tutorials) (https://www.youtube.com/watch?v=tPYj3fFJGjk
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PyTorch particularly popular for NLP and computer vision tasks. - PyTorch Documentation - PyTorch Tutorial on YouTube
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Keras: A high-level API for building and training deep learning models, Keras is user-friendly and runs on top of TensorFlow, making it easier to experiment with deep learning. - [Keras Documentation](https://keras.io/guides/) - [Keras Tutorial](https://www.tensorflow.org/guide/keras)
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Pandas6: Essential for data manipulation and analysis, Pandas provides data structures and functions needed to work with structured data seamlessly. - [Pandas Documentation](https://pandas.pydata.org/docs/) - [Pandas Tutorial](https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python)
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NumPy: A fundamental package for numerical computations in Python, NumPy supports large multi-dimensional arrays and matrices, along with a collection of mathematical functions. - [NumPy Documentation](https://numpy.org/doc/stable/) - [NumPy Tutorial](https://www.datacamp.com/community/tutorials/python-numpy-tutorial) 5
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Matplotlib and Seaborn Matplotlib is excellent for creating static plots, while Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics. - [Matplotlib Documentation](https://matplotlib.org/stable/contents.html) - [Seaborn Documentation](https://seaborn.pydata.org/tutorial.html)
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OpenCV A powerful library for computer vision, OpenCV can process images and videos to identify objects, faces, and even handwriting. - [OpenCV Documentation](https://docs.opencv.org/master/) - [OpenCV Tutorial](https://www.pyimagesearch.com/start-here/)