This repository contains implementations of various machine learning algorithms categorized into different folders such as classification and regression. Each folder contains well-documented code, example datasets, and usage instructions for the included algorithms.
This repository contains implementations of foundational machine learning algorithms categorized into classification and regression sections...
My journey into machine learning began with Andrew Ng’s renowned Machine Learning course, where I have completed the first ten videos covering fundamental concepts such as supervised learning, logistic regression, and empirical risk minimization. These lessons sparked my passion for understanding the theory behind algorithms and inspired me to implement core models like Gaussian Discriminant Analysis and Softmax Regression from scratch.
This repository represents my hands-on effort to deepen my knowledge by translating theory into practice, especially focusing on classification and regression problems with real-world datasets like personality prediction and raisin classification. By working through the challenges of data preprocessing, algorithm implementation, and model evaluation, I have strengthened my foundational skills and developed a practical understanding of how machine learning works beyond theory.
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classification/
Contains algorithms for supervised classification tasks, such as Logistic Regression, Softmax Regression, Gaussian Discriminant Analysis (GDA), Naive Bayes, and others. -
regression/
Contains algorithms focused on regression tasks, including Linear Regression, Locally Weighted Regression, and related models.
Additional folders and algorithms will be added over time to expand the repository.
Each folder has its own README with details on the algorithms implemented, setup instructions, and example usage. Please refer to those for more information.