├── 3_month_interview_process.png ├── LICENSE └── README.md /3_month_interview_process.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/whaleonearth/MLE-DS-Interview-Prep-Guide/HEAD/3_month_interview_process.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 whaleOnearth 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning GenAI Interview Preparation Guide 2 | 3 | ## Contents 4 | 5 | - [📋 Strategic Overview](#-strategic-overview) 6 | - [Optimization Strategy](#optimization-strategy) 7 | - [💻 Part 1: Coding](#-part-1-coding) 8 | - [Data Structure and Algorithms Problems](#data-structure-and-algorithms-problems) 9 | - [Concepts Review](#concepts-review) 10 | - [Coding Questions List](#coding-questions-list) 11 | - [Machine Learning Coding](#machine-learning-coding) 12 | - [🧠 Part 2: Machine Learning Design](#-part-2-machine-learning-design) 13 | - [📚 Part 3: ML/Deep Learning/genAI Concepts](#-part-3-mldeep-learninggenai-concepts) 14 | - [Machine Learning](#machine-learning) 15 | - [Data Science](#data-science) 16 | - [LLM/genAI](#llmgenai) 17 | - [Transformer](#transformer) 18 | - [AI Agent](#ai-agent) 19 | - [Awesome GenAI Review](#awesome-genai-review-if-you-have-more-time-) 20 | - [Fast Paper Summary Tool](#fast-paper-summary-tool) 21 | - [📝 Resume Preparation using AI](#-resume-preparation-using-ai) 22 | - [🎬 Mock Interview](#-mock-interview) 23 | - [🗓️ 3-Month Interview Preparation Timeline](#-3-month-interview-preparation-timeline) 24 | - [Month 1: Foundations](#month-1-foundations) 25 | - [Month 2: Building & Applying](#month-2-building--applying) 26 | - [Month 3: Specialization & Interviews](#month-3-specialization--interviews) 27 | - [Key Milestones](#key-milestones) 28 | - [Behavioral Interview Preparation](#behavioral-interview-preparation) 29 | 30 | 31 | ## 📋 Strategic Overview 32 | 33 | This comprehensive THREE month preparation framework is designed for Machine Learning Engineers, ML Scientists, and Applied/Data Scientists aiming to excel in technical interviews. The structured approach addresses four critical assessment areas: Algorithmic Coding, Machine Learning System Design, Technical ML/AI Concepts, and Professional Behavioral Assessment. 34 | 35 | ![alt text](3_month_interview_process.png?raw=true) 36 | 37 | ### Optimization Strategy 38 | 39 | This evidence-based preparation timeline delivers: 40 | 41 | - **50%+ Efficiency Improvement** through targeted, high-impact learning pathways 42 | - **Strategic Topic Prioritization** eliminating low-yield subject areas 43 | - **Progressive Skill Development** from foundational to specialized expertise 44 | - **Application Timing Optimization** aligned with your preparation lifecycle 45 | - **Continuous Improvement Methodology** leveraging early interview experiences 46 | ## 💻 Part 1: Coding 47 | 48 | ### Data Structure and Algorithms Problems 49 | 50 | #### Concepts Review 51 | - [AlgoMonster](https://algo.monster/dashboard) - Pattern-based algorithm interview preparation 52 | * **[Keyword to Algorithm Guide](https://algo.monster/problems/keyword_to_algo)**: Learn to map problem descriptions to appropriate solution techniques 53 | * **[Algorithm Templates](https://algo.monster/templates)**: Reusable code frameworks for common patterns like binary search, BFS/DFS, and dynamic programming 54 | 55 | - [HelloInterview](https://www.hellointerview.com/learn/code) - Visual algorithm learning platform 56 | * **Interactive Visualizations**: Animated walkthroughs showing algorithm execution in real-time 57 | * **Curated Key Questions**: Essential interview problems tagged by company and difficulty level 58 | * Features comprehensive step-by-step explanations with visual aids for complex algorithms 59 | * Covers core patterns like tree traversals, dynamic programming, and graph algorithms with visual clarity 60 | * Includes solution approaches and common techniques used at top tech companies 61 | #### Coding Questions List 62 | - [NeetCode 150](https://neetcode.io/practice) - Curated list of essential LeetCode problems 63 | * **Blind 75**: Core problems for essential patterns 64 | * **NeetCode 150**: Comprehensive coverage for most interviews 65 | * **NeetCode 250**: Advanced preparation for top tech companies 66 | * Features excellent video explanations demonstrating how to verbalize your thought process during interviews (particularly valuable for Meta-style interviews) 67 | * Problems organized by pattern with detailed solution walkthrough videos 68 | * Teaches interview communication skills alongside technical solutions 69 | - [LeetCode Company Tags](https://leetcode.com/company/) - Filter problems by company 70 | - [Bugfree.ai](bugfree.ai) - Platform specializing in debugging LeetCode solutions and summarizing optimal approaches by question type, helping users understand algorithmic patterns and common pitfalls while providing concise explanations of efficient solutions for technical interviews 71 | - [Flashcard](https://github.com/ayorgo/leetcode-neetcode-anki) - Review Leetcode anywhere and any time 72 | - Data manipulation (Optional) 73 | * [Pandas/SQL](https://leetcode.com/studyplan/top-sql-50/) 74 | ### Machine Learning Coding 75 | - [DeepML](https://www.deep-ml.com/) - Interactive platform for learning and practicing machine learning concepts from scratch 76 | * Features a LeetCode-style interface for immediate feedback and testing 77 | * Problems organized by difficulty level and concept categories 78 | - [ML-From-Scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Comprehensive collection of machine learning algorithms implemented from scratch in Python 79 | - [Practice ML Collection](https://github.com/xbeat/Machine-Learning) - Extensive compilation of ML implementations and code examples for practical applications 80 | * **Pros**: Excellent resource for practical ML implementation in startup environments 81 | * **Cons**: Lacks clear categorization, requiring specific keyword searches to find relevant content such as pytorch, EDA, machine learning, data cleaning, neural network, outlier detection, etc. 82 | 83 | ## 🧠 Part 2: Machine Learning Design 84 | - **Machine Learning System Design Interview** - Alex Xu, Zhe Li, Dinghan Shen (ByteByteGo, 2021) - Comprehensive guide for ML system design interviews covering essential concepts and frameworks for designing scalable ML systems in production. [Amazon](https://www.amazon.com/Machine-Learning-System-Design-Interview/dp/1736049127/) 85 | * Read Through all reference links after every session. 86 | - [ML Design Mock](https://www.meetapro.com/explore?q=machine&utm_source=redditexperienceddevs) - Get feedback from real FANNG comapnies. 87 | - [500 ML Design Cases by Evidently](https://www.evidentlyai.com/ml-system-design) 88 | * Real-world ML and LLM systems 89 | 90 | ## 📚 Part 3: ML/Deep Learning/genAI Concepts 91 | 92 | ### Machine Learning 93 | - [Machine Learning Glossary](https://ml-cheatsheet.readthedocs.io/en/latest/) 94 | - [ML Interview Questions with Code](https://www.educatum.com/ai-ml-interview-questions-classic) 95 | - [MLE Flashcard](https://github.com/b7leung/MLE-Flashcards) 96 | ### Data science 97 | - [Probability Cheatsheets](https://github.com/wzchen/probability_cheatsheet) 98 | - [Data Science Cheatsheets](https://github.com/khanhnamle1994/cracking-the-data-science-interview?tab=readme-ov-file) 99 | - **Ace the Data Science Interview** Nick Singh & Kevin Huo (2021) - Comprehensive guide covering SQL, statistics, probability, ML, and product metrics with 201 interview questions and solutions to help data scientists prepare effectively for technical and behavioral interviews. [Website](https://qualified.one/books/ace-the-data-science-interview/) 100 | - **A/B Testing** 101 | * [A Summary of Udacity A/B Testing Course](https://medium.com/data-science/a-summary-of-udacity-a-b-testing-course-9ecc32dedbb1) 102 | * [Experimentation Paper](https://github.com/jgamper/experimentation-resources) 103 | - **Causal Inference** 104 | * [Causal Inference Paper](https://github.com/matthewvowels1/Awesome-Causal-Inference) 105 | * [Causal Inference 101 Video](https://www.youtube.com/watch?v=Od6oAz1Op2k&list=PLTl9hO2Oobd-fjdLwLiIm5jbAkTv2bBPE) 106 | ### LLM/genAI 107 | - [LLM Interview Questions](https://www.educatum.com/llm-and-genai-advanced-interview-questions) 108 | - [Hands-On Large Language Models](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) 109 | - [AI Flashcard](https://aiflashcards.com/) 110 | #### Transformer 111 | - [Transformer Math](https://blog.eleuther.ai/transformer-math/) 112 | - [Transformer from Scrach](https://github.com/jsbaan/transformer-from-scratch) 113 | - **Transformers Explained Visually** 114 | * [Part1](https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452/) 115 | * [Part2](https://towardsdatascience.com/transformers-explained-visually-part-2-how-it-works-step-by-step-b49fa4a64f34/) 116 | * [Part3](https://towardsdatascience.com/transformers-explained-visually-part-3-multi-head-attention-deep-dive-1c1ff1024853/) 117 | ### AI Agent 118 | - [Prompt Engineering Interview Questions](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering) 119 | - [LLM RAG Interview Questions](https://github.com/jxzhangjhu/Awesome-LLM-RAG?tab=readme-ov-file) 120 | - [Awesome genAI guide](https://github.com/aishwaryanr/awesome-generative-ai-guide) 121 | ### Awesome GenAI Review, If you have more time ... 122 | - [AI/ML 30 Day Playbook](https://www.educatum.com/the-ai-ml-interview-playbook?p=1b355925845b81379a77e36b9875a226&pm=s) 123 | - [GenAI 5 Week by Hand](https://www.educatum.com/genai-by-hand-llm-study-plan) 124 | ### Fast Paper Summary Tool 125 | - [NotebookLM](https://notebooklm.google/) - Google's AI-powered notebook that summarizes and organizes research papers 126 | - [Zotero + AI plugins](https://www.zotero.org/) - Reference manager with AI plugins for summarizing and annotating papers 127 | 128 | 129 | ## 📝 Resume Preparation using AI 130 | - [Resume Editing](https://bugfree.ai/resume) 131 | * Highlight keywords matching with job descriptions. 132 | ## 🎬 Mock Interview 133 | - [HelloInterview](https://www.hellointerview.com/) 134 | - [ML Design Mock](https://www.meetapro.com/explore?q=machine&utm_source=redditexperienceddevs) 135 | 136 | 137 | 138 | 139 | ## 🗓️ 3-Month Interview Preparation Timeline 140 | 141 | #### Month 1: Foundations 142 | 143 | | Week | Focus Area | Tasks | Status | 144 | |------|------------|-------|--------| 145 | | 1-2 | Data Structures & Algorithms | • Review arrays & strings
• Review linked lists
• Review stacks & queues
• Start NeetCode 150: [Two Sum](https://leetcode.com/problems/two-sum/), [Valid Parentheses](https://leetcode.com/problems/valid-parentheses/), [Merge Two Sorted Lists](https://leetcode.com/problems/merge-two-sorted-lists/) | 📅 Scheduled | 146 | | 1-2 | ML Coding | • Basic NumPy & Pandas exercises
• Implement data preprocessing functions
• Practice ML algorithms implementation | 📅 Scheduled | 147 | | 3-4 | Data Structures & Algorithms | • Review trees & graphs: [Maximum Depth of Binary Tree](https://leetcode.com/problems/maximum-depth-of-binary-tree/)
• Review dynamic programming: [Climbing Stairs](https://leetcode.com/problems/climbing-stairs/)
• Continue NeetCode 150: [Coin Change](https://leetcode.com/problems/coin-change/) | 📅 Scheduled | 148 | | 3-4 | ML Coding | • Implement models from scratch (linear regression, decision trees)
• Feature engineering practice
• Cross-validation implementation | 📅 Scheduled | 149 | 150 | #### Month 2: Building & Applying 151 | 152 | | Week | Focus Area | Tasks | Status | 153 | |------|------------|-------|--------| 154 | | 5-6 | LeetCode Practice | • Continue NeetCode 150: [Course Schedule](https://leetcode.com/problems/course-schedule/), [Longest Substring Without Repeating Characters](https://leetcode.com/problems/longest-substring-without-repeating-characters/)
• Start company-specific problems: [Google Tagged](https://leetcode.com/company/google/), [Meta Tagged](https://leetcode.com/company/facebook/)
• Focus on medium difficulty problems | 📅 Scheduled | 155 | | 5-6 | Job Applications | • Resume optimization & ATS testing
• LinkedIn/GitHub portfolio updates
• Start applying to mid-tier companies | 📅 Scheduled | 156 | | 5-6 | ML Concepts | • Review ML fundamentals
• Study ML design patterns
• Begin ML system design practice | 📅 Scheduled | 157 | | 7-8 | LeetCode Practice | • Complete advanced NeetCode problems: [Word Break](https://leetcode.com/problems/word-break/), [Meeting Rooms II](https://leetcode.com/problems/meeting-rooms-ii/)
• Continue company-specific LeetCode: [Amazon Tagged](https://leetcode.com/company/amazon/)
• Mock coding interviews | 📅 Scheduled | 158 | | 7-8 | Job Applications | • Research dream companies
• Apply to dream companies
• Network with employees at target companies | 📅 Scheduled | 159 | | 7-8 | ML & GenAI | • Study Transformers & LLM concepts
• Review GenAI interview questions
• Practice ML case studies | 📅 Scheduled | 160 | 161 | #### Month 3: Specialization & Interviews 162 | 163 | | Week | Focus Area | Tasks | Status | 164 | |------|------------|-------|--------| 165 | | 9-10 | Mock Interviews | • [HelloInterview](https://www.hellointerview.com/) ML system design practice
• [MeetaPro](https://www.meetapro.com/) ML design mocks
• Behavioral interview practice with experienced mentor | 📅 Scheduled | 166 | | 9-10 | Interview Practice | • Company-specific research (products, tech stack)
• Initial interviews with mid-tier companies
• Refine behavioral STAR stories | 📅 Scheduled | 167 | | 9-10 | ML Concepts | • Deep learning fundamentals
• Production ML systems
• Evaluation metrics & model deployment | 📅 Scheduled | 168 | | 11-12 | Advanced Interviews | • Interviews with dream companies
• Final company-specific preparation
• Interview retrospectives & adjustments | 📅 Scheduled | 169 | | 11-12 | Mock Interviews | • Company-specific practice sessions
• End-to-end interview simulation (coding + ML design + behavioral)
• Focus on weak areas from prior interviews | 📅 Scheduled | 170 | | 11-12 | ML Specialization | • Company-specific technologies
• ML ethics and responsible AI
• Final review of flashcards and key concepts | 📅 Scheduled | 171 | 172 | #### Key Milestones 173 | 174 | | Milestone | Target Date | Status | 175 | |-----------|-------------|--------| 176 | | Complete DS&A Review | End of Week 4 | 📅 Scheduled | 177 | | Finish [NeetCode 150](https://neetcode.io/practice) | End of Week 8 | 📅 Scheduled | 178 | | Resume & Portfolio Ready | End of Week 6 | 📅 Scheduled | 179 | | 10 Mock Interviews Completed | End of Week 10 | 📅 Scheduled | 180 | | 5 Behavioral Mock Interviews | End of Week 8 | 📅 Scheduled | 181 | | STAR Stories Prepared | End of Week 7 | 📅 Scheduled | 182 | | First Applications Sent | Mid-Week 6 | 📅 Scheduled | 183 | | Dream Company Applications | End of Week 8 | 📅 Scheduled | 184 | | First Round Interviews | Weeks 9-10 | 📅 Scheduled | 185 | | Dream Company Interviews | Weeks 11-12 | 📅 Scheduled | 186 | 187 | #### Behavioral Interview Preparation 188 | 189 | | Category | STAR Stories to Prepare | Example Questions | 190 | |----------|------------------------|-------------------| 191 | | Leadership & Initiative | • Project turnaround
• Team motivation
• Process improvement | • Tell me about a time you led a project
• Describe a situation where you influenced without authority
• How have you improved a process? | 192 | | Problem Solving | • Technical debugging
• Resource constraints
• Ambiguous requirements | • Describe a difficult technical problem you solved
• Tell me about working with incomplete information
• How did you handle a project with tight deadlines? | 193 | | Teamwork & Collaboration | • Cross-functional project
• Difficult team member
• Remote collaboration | • How do you work with non-technical stakeholders?
• Tell me about resolving a conflict with a teammate
• Describe a successful collaboration | 194 | | Failure & Resilience | • Failed project
• Missed deadline
• Learning from mistakes | • Tell me about a time you failed
• How do you handle criticism?
• Describe overcoming a significant setback | 195 | | ML-Specific | • Model underperformance
• Ethical ML challenge
• ML research to production | • How did you improve a failing model?
• Tell me about considering fairness in ML
• Describe deploying research to production | 196 | 197 | **Legend:** 198 | - 📅 Scheduled 199 | - ⏳ In Progress 200 | - ✅ Completed 201 | --------------------------------------------------------------------------------