A collection of (not only) Jupyter Notebooks covering a wide range of topics, including machine learning, data exploration, numerical methods, and deep learning. Hands-on exercises, tutorials, and projects designed to enhance skills and understanding of key concepts in data science. Whether you're a fellow learner or an experienced practitioner, I hope you find these resources valuable in your own data science endeavors.
The repository is organized into the following directories, each focusing on a specific topic:
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Machine Learning: Exploring machine learning algorithms, techniques, and applications. Includes tutorials, exercises, and projects implementing machine learning models.
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Data Exploration: Preprocessing, visualize=ation, and gaining insights from datasets using popular libraries such as Pandas and Matplotlib.
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Numerical Methods: Computational techniques used in data science. From linear algebra to optimization algorithms, practical implementations and examples.
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Neural Netros: Dive into the field of NN and deep learning. Exploreing neural networks, convolutional networks, recurrent networks, and other advanced architectures through tutorials and hands-on projects.
Each directory contains a variety of Jupyter Notebooks, tutorials, and / or mini-projects focusing on the respective topic.