Welcome!
Welcome to the ultimate repository for mastering Machine Learning, Deep Learning, and MLOps. This collection is engineered to take you from foundational concepts to production-grade system design, tailored specifically for L4/L5+ ML Engineer roles at top-tier tech companies.
Learning Paths
Core algorithms, math, and concepts. Start here for the "How it works" level of depth.
Supervised Learning - Regression, Classification, Ensembles.
Unsupervised Learning - Clustering, Dimensionality Reduction.
Data Preprocessing - The ML Pipeline foundation.
From Neural Network basics to State-of-the-Art Transformers and Generative Models.
Core Components - Layers, Optimizers, Regularization.
Architectures - CV, NLP, Time Series.
Advanced Topics - MCP Protocol, Scaling.
The "Closer" for senior interviews. Real-world architectures and engineering trade-offs.
ML Engineering - Production principles.
Design Patterns - Architectural solutions.
Design Interview - Master frameworks.
High-signal revision and practice material.
Revision Prep - The "Night Before" technical summary.
Performance & Reference
ML Glossary - A-Z of ML terms with formulas and examples.
MLOps Guide - Deployment, monitoring, and lifecycle management.
30-Day Roadmap - A structured learning journey.
Technical Standards
Every file in this repository is maintained to a high technical standard:
Mathematical Rigor: Formal formulas and complexity analysis.
Code Implementation: Clean Python/Scikit-Learn/PyTorch snippets.
Interview Focused: "Executive Summaries" and "Interview Tips" in every major file.
Production Ready: Real-world trade-offs and engineering constraints.
“Production ML is 10% modeling and 90% engineering.”
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