Interview Prep

This portal is your one-stop shop for Machine Learning interviews at top-tier tech companies. It covers everything from entry-level basics to senior system architecture.

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Target by Role & Level

Level	Files to Focus On
Junior (L3)	Q&A Bank, Cheat Sheet, Statistics
Mid (L4)	Above + System Design, Coding, Scenarios
Senior (L5+)	Above + Math Derivations, DL Architectures, NLP/Transformers

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Complete File Index

Core Q&A

• Master Q&A Bank – Tiered questions (Level 1-3)

• Statistics & Probability – Bayes, Distributions, Hypothesis Testing, MLE

Technical Deep Dives

• Deep Learning Architectures – CNNs, RNNs, Transformers, GANs, Object Detection

• NLP & Transformers – BERT, GPT, Attention, Text Preprocessing

Practical Application

• ML Coding Patterns – Pipelines, NumPy, PyTorch templates

• System Design Case Studies – RecSys, Fraud, Search

• Practical ML Scenarios (Master Guide) – 50+ In-depth "What would you do if..." cases

• Scenario-Based Quick Questions – High-signal practice cases

Foundations & Strategy

• Quick Revision Cheat Sheet – Algorithms & Metrics

• Math & Derivations – Whiteboard proofs

• Interview Strategy Guide – Behavioral, STAR method

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Emergency Study Plan

Time Left	What to Do
12 hours	Read all files. Practice Coding Patterns once.
6 hours	Q&A Bank + System Design + Scenarios
3 hours	Cheat Sheet + Statistics + DL Architectures
1 hour	Cheat Sheet + Math Derivations
30 min	Review STAR stories in Strategy Guide

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Topic Coverage Checklist

• ML Fundamentals (Bias-Variance, Regularization)

• Supervised Learning (Regression, Classification, Ensembles)

• Unsupervised Learning (Clustering, PCA)

• Deep Learning Architectures (CNN, RNN, Transformer, GAN)

• NLP (Tokenization, Embeddings, BERT, GPT)

• Statistics & Probability (Bayes, Distributions, MLE)

• Model Evaluation (Metrics, Cross-Validation)

• System Design (RecSys, Fraud, Search)

• MLOps (Monitoring, Drift, Deployment)

• Coding Patterns (Sklearn, NumPy, PyTorch)

• Scenario-Based Questions (Debugging, Trade-offs)

• Math Derivations (Gradient Descent, PCA)

• Behavioral (STAR method)

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**You are fully prepared. Trust your training. Good luck! **