This repository offers a structured, three-month preparation framework for Machine Learning Engineers, ML Scientists, and Applied/Data Scientists aiming to excel in technical interviews. It addresses key assessment areas—algorithmic coding, ML system design, technical ML/AI/GenAI concepts, and behavioral skills—to enhance interview efficiency and strategic topic prioritization.

How It Works

The guide employs an evidence-based, phased approach over three months, prioritizing high-impact learning pathways and strategic topic selection. It breaks down preparation into foundational, building/applying, and specialization/interview stages, integrating continuous improvement through early interview feedback and progressive skill development. The framework covers essential areas like data structures and algorithms, ML system design, deep learning, GenAI, and resume preparation.

Quick Start & Requirements

This repository is a guide and does not require software installation. Users need dedication to follow the 3-month timeline. It references external tools and resources such as LeetCode, AlgoMonster, HelloInterview, and various books/papers, which may have their own setup requirements (e.g., Python environments, specific libraries). The guide itself requires access to the internet to utilize these external resources.

Highlighted Details

• A detailed 3-month timeline with weekly focus areas and key milestones for DS&A, ML coding, ML concepts, and job applications.
• Curated lists of essential coding problems (NeetCode 150, Blind 75) with links to video explanations and pattern-based learning resources (AlgoMonster).
• Resources for ML System Design, including a comprehensive guide and mock interview platforms.
• Coverage of ML/Deep Learning/GenAI concepts with links to glossaries, flashcards, and papers on Transformers and LLMs.
• Dedicated sections for resume preparation using AI and mock interviews, including behavioral interview preparation with STAR story examples.

Maintenance & Community

The provided README does not contain information regarding maintainers, community channels (e.g., Discord, Slack), sponsorships, or project roadmap.

Licensing & Compatibility

The README does not specify a software license. Therefore, its terms of use, distribution, and compatibility for commercial or closed-source projects are unclear.

Limitations & Caveats

This guide serves as a curated roadmap and does not guarantee job placement; user effort and external resource utilization are critical. It aggregates links to other resources, and the quality, availability, or specific requirements of those external resources are beyond the scope of this guide. Some referenced resources may have their own limitations, such as the "ML-From-Scratch" section noting a lack of clear categorization.