Interview Ready · 2025+

Master AI & ML Interviews

100+ expert-curated Q&As across 5 comprehensive guides — from ML fundamentals to Claude Code, GenAI, Agentic AI, and Prompt Engineering.

100+Questions
5Guides
AllLevels
2025Updated
Start with Fundamentals → View on GitHub ↗

Study Guides

🧠
AI/ML Fundamentals

Supervised & unsupervised learning, deep learning, NLP, computer vision, reinforcement learning, and model evaluation.

35 Q&As Study now →
✨
Generative AI

LLMs, Transformers, BERT & GPT architectures, RAG, fine-tuning, RLHF, prompt engineering, and AI safety.

22 Q&As Study now →
🤖
Agentic AI Systems

Agent architectures, ReAct, tool use, planning & reasoning, memory management, MCP, multi-agent coordination, and security.

25 Q&As Study now →
💬
Prompt Engineering

Prompt design principles, Chain-of-Thought, few-shot learning, advanced techniques, optimization strategies, and common pitfalls.

15 Q&As Study now →
⚡
Claude Code

Claude CLI, agentic loops, CLAUDE.md memory, hooks, subagents, MCP, Plan Mode, worktrees, headless mode, and interview one-liners.

Interview Ready Study now →

Learning Paths by Role

🔧 ML Engineer
ML Fundamentals → Generative AI → Prompt Engineering
🔬 AI Researcher
ML Fundamentals → Generative AI → Agentic AI
💬 Prompt Engineer
Generative AI → Prompt Engineering → Agentic AI
🚀 Full-Stack AI
All 5 guides — start with Fundamentals
⚡ AI Developer
Agentic AI → Claude Code → Prompt Engineering
🛡️ AI Safety / Red Team
Generative AI → Agentic AI → Prompt Engineering

How to Use These Guides

1Read Q&As in order — they build on each other progressively.
2Explain without looking — practice out loud to test real retention.
3Code along — implement examples shown to solidify understanding.
4Connect to real projects — anchor each concept to a practical use case.
5Think out loud — in interviews, narrate your reasoning step by step.
6Clarify before answering — ask the interviewer to narrow ambiguous questions.

Topics Covered

Linear Regression Logistic Regression Decision Trees Random Forest SVM K-Means PCA Neural Networks CNNs RNNs & LSTMs Attention Mechanisms Transformers BERT GPT Tokenization Word Embeddings Object Detection Diffusion Models Q-Learning Policy Gradients Large Language Models Fine-tuning RAG RLHF AI Safety Alignment Agent Architectures ReAct Tool Use Planning Memory Management MCP Multi-Agent Systems Chain-of-Thought Few-Shot Learning Prompt Optimization Claude Code Agentic Loops Hooks Subagents Worktrees Plan Mode Evaluation Metrics Bias & Variance Cross-Validation Regularization Prompt Caching System Design