This repository is a comprehensive collection of educational tutorials that implement machine learning algorithms and applications entirely from scratch using only NumPy. It covers a vast range of topics including core ML algorithms (linear regression, SVM, K-means, PCA, etc.), neural networks (CNN, RNN, LSTM, GAN, transformer), computer vision (object detection, face recognition, OCR), natural language processing (sentiment analysis, chatbots, text summarization, BPE tokenizer), time series forecasting (ARIMA, LSTM, Prophet), recommendation systems, anomaly detection, and reinforcement learning. Each tutorial is a self-contained Python script that generates synthetic data with planted structure, trains a hand-coded model, and validates against naive baselines. The code is heavily commented and designed for teaching, not production. There are no real datasets, no external dependencies beyond NumPy, and no shared library—each file is independent, leading to significant code duplication across tutorials. The repository serves as an excellent learning resource for understanding the inner workings of ML algorithms, but it is not suitable for direct reuse in production systems without substantial refactoring.
This repository is a curated collection of step-by-step tutorials for building various technologies from scratch, such as 3D renderers, databases, operating systems, programming languages, and more. It is not a software application or data pipeline; it is a reference list of external resources. The repository itself contains no executable code, only a README with categorized links and an issue template. It serves as a community-driven directory for learning by building, maintained by CodeCrafters, Inc. The codebase is essentially a single markdown file with no software architecture, dependencies, or reusable components.