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Comprehensive ML/AI interview codex with iterative system design, production-ready code, and 2026 standards. Includes LLM/GenAI, RAG systems, agentic AI, and algorithms from scratch.
| Date | Stars |
|---|---|
| 2026-07-31 | 476 |
| 2026-08-06 | 476 |
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# ML/AI Interview Preparation - Complete Guide A comprehensive collection of machine learning and AI interview preparation materials, covering ML coding, system design, LLM/GenAI, and DSA. ## Repository Overview This repository contains battle-tested interview preparation materials for ML/AI engineering roles, including: - **Machine Learning**: Algorithms from scratch, coding problems, production ML systems - **LLM/GenAI**: Production LLMs, RAG systems, embeddings, attention mechanisms - **System Design**: Agentic AI systems, distributed ML, scalable architectures - **DSA**: Data structures and algorithms for ML engineers - **MLOps**: Production best practices, monitoring, deployment ## Quick Start ### For ML Coding Interviews 1. Start with [ML Coding Interview Master Guide](ml/ML-CODING-INTERVIEW-MASTER-GUIDE.md) 2. Practice with [ML Algorithms from Scratch](ml/ml-algorithms-from-scratch.ipynb) 3. Review [Neural Network Components](deep-learning/neural-network-components-from-scratch.ipynb) ### For System Design Interviews 1. Read [LLM/ML System Design Master Guide](gen-ai/LLM-ML-SYSTEM-DESIGN-MASTER-GUIDE.md) 2. Study iterative examples: - [Agentic AI Customer Support](agentic-ai/agentic-ai-iterative-interview.md) - [AI Code Review System](agentic-ai/code-review-ai-iterative-interview.md) 3. Study [Model Context Protocol (MCP)](agentic-ai/mcp-interview-preparation-guide.md) - Universal AI-tool integration standard 4. Review [System Design Examples](system-design/system-design-examples-enhanced.md) ### For LLM/GenAI Roles 1. Start with fundamentals: - [LLM Fundamentals Part 1: Tokenization & Context](gen-ai/llm-fundamentals-part1-tokenization-context.md) - [LLM Fundamentals Part 2: Inference & Optimization](gen-ai/llm-fundamentals-part2-inference-optimization.md) 2. [LLM Production Complete Guide](gen-ai/llm-production-complete-guide.md) 3. [Production RAG Systems](gen-ai/production-rag-systems-guide.md) 4. [Embedding Models Guide](gen-ai/embedding-models-comprehensive-guide.md) 5. [LoRA/QLoRA Fine-tuning](gen-ai/lora-qlora-finetuning-guide.ipynb) 6. **Model Context Protocol (MCP):** - [MCP Interview Preparation Guide](agentic-ai/mcp-interview-preparation-guide.md) - [MCP Hands-On Implementation](agentic-ai/mcp-hands-on-implementation.ipynb) - [MCP Enterprise Banking Use Case](agentic-ai/mcp-enterprise-banking-use-case.md) - [MCP Production Best Practices](agentic-ai/mcp-production-best-practices.md) ### For MLOps/Production ML 1. [MLOps Production Guide](ml/mlops-production-ml-guide.md) 2. [Feature Engineering Guide](ml/feature-engineering-guide.md) ## Complete Content Index ### Machine Learning Fundamentals #### ML Coding & Algorithms - **[ML Coding Interview Master Guide](ml/ML-CODING-INTERVIEW-MASTER-GUIDE.md)** - Complete guide to ML coding interviews - **[ML Algorithms from Scratch](ml/ml-algorithms-from-scratch.ipynb)** - Implement core ML algorithms: - Linear Regression, Logistic Regression - Decision Trees, Random Forest - K-Means, KNN - Naive Bayes, SVM - Gradient Descent variants - **[ML Coding Problems](ml/ml-coding-problems.ipynb)** - Practice problems with solutions #### Decision Trees - **[Decision Trees Complete Guide](ml/decision-trees-complete-guide.md)** - Complete guide to decision trees: - Tree construction algorithms (CART, ID3, C4.5) - Splitting criteria (Entropy, Gini, Information Gain) - Pruning techniques (pre-pruning, post-pruning) - Regression trees and variance reduction - Feature importance (MDI, permutation) - Tree visualization methods - Implementation from scratch - 25+ interview questions with answers #### Ensemble Methods - **[Bagging Ensemble Methods](ml/bagging-ensemble-methods-guide.md)** - Bootstrap Aggregating complete guide: - Bootstrap sampling, OOB error - Random Forest deep dive - Variance reduction mechanism - Implementation from scratch - 20+ interview questions with answers - **[Boosting Ensemble Methods](m
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matched fp:75b881f07ee795f8, topic:agentic-ai, topic:langgraph, desc:agentic