Mon Notebook sur les fondamentaux du Machine Learning
A collection of Jupyter Notebook exercises covering key statistical concepts for Data Science, featuring interactive visualizations and real-world datasets.
A coursework-based repository under the guidance of Prof. Ratnakar Dash, focusing on implementing machine learning algorithms, data preprocessing, model evaluation, and hyperparameter tuning. Includes Jupyter notebooks with hands-on experiments.
A fully-featured batteries-included Neovim distribution for the world of Data Science. Prepared to run code and interact with Jupyter Notebooks without ever leaving your terminal.
📓 Simulation exercises access point for result extraction which can be used in modeling and theoretical approach of molecular and atomic processes.
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