This repository is a hands-on ML engineering reference built around one principle: the best way to learn machine learning is to build complete, working systems. Every project follows the same reproducible architecture — dataset handling, preprocessing pipelines, trained models, Jupyter notebooks, and a deployable Flask API — so you spend time learning ML concepts rather than figuring out folder layouts. The repository powers two external learning platforms and serves as a backend reference for reproducible ML workflows.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/dronabopche/100-ML-AI-Project/llms.txt
Use this file to discover all available pages before exploring further.
Two sections, one system
The repository is organized into two top-level sections:ML_To_Train/— 100+ end-to-end ML projects covering supervised learning, unsupervised learning, computer vision, NLP, generative AI, conversational AI, time series, and reinforcement learning. Each project is self-contained with its own dataset, trained model artifacts, preprocessing pipeline, and Flask API.ML_from_scratch/— Implementations of ML algorithms built from first principles without high-level library abstractions, designed to build deep understanding of how models work internally.
Where to go next
Project structure
Understand the standard directory layout shared by every project in the repo.
Quickstart
Clone the repo, install dependencies, and make your first prediction in minutes.
ML from scratch
Explore algorithm implementations built without high-level ML abstractions.
Supervised learning projects
Browse regression and classification projects using real-world tabular datasets.
Project categories
TheML_To_Train/ section covers nine categories across the full ML spectrum:
| Category | Range | Description |
|---|---|---|
| Supervised Learning | 01–29 | Regression and classification on tabular data |
| Unsupervised Learning | 09, 24–25 | Clustering, anomaly detection, topic modeling |
| Recommendation Systems | 22 | Personalized recommendation models |
| Computer Vision & Deep Learning | 30–34 | Image classification using CNNs |
| Natural Language Processing | 40–41, 16 | Text analysis and classification |
| Generative AI | 50–52, 87 | Text and image generation systems |
| Conversational AI & Chatbots | 60–65 | Rule-based and AI-powered chat systems |
| Time Series & Forecasting | 70, 72 | Sequential and temporal data modeling |
| Reinforcement Learning | 71, 80–86 | Agent-based learning and game environments |