This repository is dedicated to revising and exploring fundamental machine learning concepts through various Jupyter notebooks. Each notebook covers a specific concept, providing code and explanations to reinforce understanding and application.
The goal of this repository is to serve as a resource for machine learning revision, covering key algorithms and techniques. It is intended for both beginners and practitioners looking to refresh their knowledge with practical implementations.
Feel free to contribute, suggest improvements, or ask questions through issues and pull requests!
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custom_kernel_svm.ipynb: Custom implementation of Kernel-based Support Vector Machine (SVM), exploring the effect of different kernels on classification tasks.
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logistic_regression.ipynb: A deep dive into Logistic Regression, focusing on its application, decision boundaries, and performance evaluation.
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pca_from_scratch_n_Scikit.ipynb: A deep dive into Prinicpal Component Analysis, focusing on core implementation using numpy as well as scikit-learn.
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decision_tree_scratch.ipynb: A complete notebook that defines trees node from scratch and implement decision trees from the very basics.
- Linear Regression
- Support Vector Machines
- K Means clustering