build-your-own-x-machine-learning

Overview of build-your-own-x-machine-learning

What it is

This repository is a collection of tutorials and implementations for building machine learning models from scratch. It is maintained by Amit Shekhar, founder of Outcome School. The project aims to cover topics from linear regression to deep learning and large language models (LLMs).

Key features

• Covers core machine learning algorithms implemented from scratch, including linear regression, logistic regression, decision trees, SVM, K-means, PCA, and more.
• Includes neural networks and deep learning projects such as building a neural network, CNN, LSTM, transformer-based LLM, autoencoder, GAN, and diffusion model from scratch.
• Provides recommendation system implementations for movies, videos, products, friends, and content feeds.
• Contains computer vision applications like traffic sign classification, OCR, face detection, object detection, and image segmentation.
• Offers natural language processing projects including language translation, text summarization, tokenizer, sentiment analysis, chatbot, and named entity recognition.
• Features time series and forecasting models using LSTM, ARIMA, Prophet, and anomaly detection.

Use cases

This repository is suitable for learners who want to understand machine learning algorithms by implementing them from scratch. It can be used as a hands-on guide for mastering fundamental concepts, preparing for technical interviews, or building a portfolio of ML projects.

Getting started

See the repository README for details.

License and pricing

Licensed under Apache-2.0. The project is open source and free to use.

Based on the project's public README and repository metadata.