Computer Vision algorithms implemented using OpenCV, NumPy and MatPlotLib, for Stanford's CS131: Computer Vision: Foundations and Applications; Stanford's CS231A: Computer Vision, From 3D Reconstruction to Recognition; Stanford's CS231n: Convolutional Neural Networks for Visual Recognition

A structured NumPy practice repository showcasing hands-on learning through Jupyter notebooks. It covers array creation, indexing, slicing, reshaping, broadcasting, and numerical operations. Built to strengthen core foundations required for Data Science, Machine Learning, and efficient scientific computing in Python.

ML Algorithms from First Principles. A comprehensive implementation of core Machine Learning algorithms and their underlying Linear Algebra foundations in pure Python/NumPy. No black-box libraries. (dont mind a latex rendering sometimes it doesnt work and the lecture vibes as well just hang on i am updating it constantly)

Data Science Foundations with Python: Learn NumPy, Pandas, Matplotlib, and Seaborn, Published by Packt Publishing

Research-oriented ML/DL foundations covering NumPy, Pandas, visualization, classical machine learning, and deep learning, with emphasis on fundamentals, reproducibility, and understanding model behavior.

This repository contains a step-by-step implementation of Softmax Regression, built entirely from scratch using NumPy. The notebook explains the mathematical foundations and algorithmic steps in detail, making it a great resource for understanding multi-class classification and the softmax function.

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This repository covers the essential mathematical foundations required for understanding and building machine learning models — with practical implementations in Python using NumPy, Pandas, and Matplotlib.

Learn AI Fundamentals. Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).

A collection of hands-on Jupyter notebooks covering essential Python libraries for Data Science and Machine Learning — including NumPy, pandas, Matplotlib, Seaborn, Scikit-learn, PyTorch, and TensorFlow. Each notebook demonstrates core concepts, real-world examples, and practical use cases to build strong foundations for ML and AI projects.

I'm learning math foundations, NumPy, and building ML algorithms step by step.

Python data science practice mini projects to build my foundations using ( NumPy, pandas, matplotlib )

Machine Learning Foundations from University of Washington, rewrite in numpy, pandas, scipy and sklearn

Building strong data analysis foundations using NumPy and Pandas with practical, beginner-friendly examples.

Learn Python, NumPy, pandas, Matplotlib, PyTorch, Calculus, and Linear Algebra—the foundations for building neural network.

production ready implementation of Linear Regression using only NumPy, demonstrating the mathematical foundations from gradient descent to scalable deployment.

A collection of AI models implemented from scratch using pure NumPy, focusing on core mathematical foundations without high-level ML libraries.

Implementation of linear regression algorithm from scratch using only NumPy. This project demonstrates the mathematical foundations of linear regression and gradient descent optimization.

Implementing ML algorithms from scratch using NumPy and pandas. Inspired by CS229, this repo is a hands-on journey into the mathematical and coding foundations of machine learning.

This project demonstrates the mathematical foundations of Computer Vision. Instead of relying on functions from OpenCV, I used Pure NumPy to manipulate images at the pixel-matrix level.

This repository documents my complete learning path—covering Python, NumPy, data preprocessing, statistics, supervised & unsupervised algorithms, model evaluation, and deployment. Building strong foundations and practical ML skills step by step

This repository implements core Machine Learning algorithms from scratch using Python and NumPy, focusing on intuition, mathematical foundations, loss functions, and optimization from first principles rather than relying on high-level ML libraries.

This repository contains all coursework, practice notebooks, and projects completed as part of the NYU Tandon Data Science Bootcamp. Topics include Python programming, NumPy, Pandas, data visualization, and machine learning foundations. Maintained by Reem Aboutaleb.

Beginner-friendly Python code examples covering essential data analytics libraries — NumPy (numerical computing), Pandas (data manipulation), Matplotlib (basic plots), and Seaborn (statistical visualization). Provides simple, practical scripts to build strong foundations in Python for data analytics.

A collection of machine learning algorithms implemented from scratch using Python and NumPy. Each project aims to demonstrate the mathematical foundations and implementation details behind popular ML models — without relying on high-level machine learning libraries.

Welcome to this beginner-friendly repository on Data Analysis, focusing on the foundations of NumPy, Pandas, Data Preprocessing, and Data Visualization using Python. This repo is perfect for those getting started with data science and machine learning projects.

MathML: Mathematical Foundations for Machine Learning A Python library that extends NumPy, SciPy, and Pandas with ML-focused mathematical functions, statistical methods, and linear algebra utilities. Designed to bridge the gap between theoretical ML mathematics and practical implementation.

"Hands-on NumPy practice covering matrix and array operations like diagonal, trace, slicing, filtering, comparisons, row/column manipulation, and 90° rotation. Includes mini projects: sales analysis, student marks, game scores, and temperature data to build AI/ML foundations

This repository covers core Python concepts, mathematical foundations for AI/ML, and small-scale projects. It includes clear explanations, practical examples, and implementations using frameworks like NumPy, Pandas, and Matplotlib. Contributions and feedback are welcome as I progress in my learning journey.

Repository for the Ciencias Naturales y Tecnología (CNYT) course at Escuela Colombiana de Ingeniería Julio Garavito. Explores quantum computing from mathematical foundations to quantum systems and algorithms (Deutsch, Grover, Shor), through hands-on Python/Jupyter labs, simulations, and experiments with NumPy, Qiskit, and IBM Quantum Composer.