Machine Learning Interviews

Book description

As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process.

Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews.

This guide shows you how to:

  • Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions
  • Assess your interests and skills before deciding which ML role(s) to pursue
  • Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process
  • Acquire the skill set necessary for each machine learning role
  • Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions
  • Prepare for interviews in statistics and machine learning theory by studying common interview questions

Table of contents

  1. Preface
    1. Why Machine Learning Jobs?
    2. Who This Book Is For
    3. What This Book Is Not
    4. Conventions Used in This Book
    5. O’Reilly Online Learning
    6. How to Contact Us
    7. Acknowledgments
  2. 1. Machine Learning Roles and the Interview Process
    1. Overview of This Book
    2. A Brief History of Machine Learning and Data Science Job Titles
    3. Job Titles Requiring ML Experience
    4. Machine Learning Lifecycle
      1. Startups
      2. Larger ML Teams
    5. The Three Pillars of Machine Learning Roles
      1. Machine Learning Algorithms and Data Intuition: Ability to Adapt
      2. Programming and Software Engineering: Ability to Build
      3. Execution and Communication: Ability to Get Things Done in a Team
      4. Clearing Minimum Requirements in the Three ML Pillars
    6. Machine Learning Skills Matrix
    7. Introduction to ML Job Interviews
    8. Machine Learning Job-Interview Process
      1. Applying for Jobs Through Websites or Job Boards
      2. Resume Screening of Website or Job-Board Applications
      3. Applying via a Referral
      4. Preinterview Checklist
      5. Recruiter Screening
      6. Overview of Main Interview Loop
    9. Summary
  3. 2. Machine Learning Job Application and Resume
    1. Where Are the Jobs?
    2. ML Job Application Guide
      1. Your Effectiveness per Application
      2. Job Referrals
      3. Networking
    3. Machine Learning Resume Guide
      1. Take Inventory of Your Past Experience
      2. Overview of Resume Sections
      3. Tailoring Your Resume to Your Desired Role(s)
      4. Final Resume Touch-ups
    4. Applying to Jobs
      1. Vetting Job Postings
      2. Mapping Your Skills and Experience to the ML Skills Matrix
      3. Tracking Applications
    5. Additional Job Application Materials, Credentials, and FAQ
      1. Do You Need a Project Portfolio?
      2. Do Online Certifications Help?
      3. FAQ: How Many Pages Should My Resume Be?
      4. FAQ: Should I Format My Resume for ATS (Applicant Tracking Systems)?
    6. Next Steps
      1. Browsing Job Postings
      2. Identifying the Gaps Between Your Current Skills and Target Roles
    7. Summary
  4. 3. Technical Interview: Machine Learning Algorithms
    1. Overview of the Machine Learning Algorithms Technical Interview
    2. Statistical and Foundational Techniques
      1. Summarizing Independent and Dependent Variables
      2. Defining Models
      3. Summarizing Linear Regression
      4. Defining Training and Test Set Splits
      5. Defining Model Underfitting and Overfitting
      6. Summarizing Regularization
      7. Sample Interview Questions on Foundational Techniques
    3. Supervised, Unsupervised, and Reinforcement Learning
      1. Defining Labeled Data
      2. Summarizing Supervised Learning
      3. Defining Unsupervised Learning
      4. Summarizing Semisupervised and Self-Supervised Learning
      5. Summarizing Reinforcement Learning
      6. Sample Interview Questions on Supervised and Unsupervised Learning
    4. Natural Language Processing Algorithms
      1. Summarizing NLP Underlying Concepts
      2. Summarizing Long Short-Term Memory Networks
      3. Summarizing Transformer Models
      4. Summarizing BERT Models
      5. Summarizing GPT Models
      6. Going Further
      7. Sample Interview Questions on NLP
    5. Recommender System Algorithms
      1. Summarizing Collaborative Filtering
      2. Summarizing Explicit and Implicit Ratings
      3. Summarizing Content-Based Recommender Systems
      4. User-Based/Item-Based Versus Content-Based Recommender Systems
      5. Summarizing Matrix Factorization
      6. Sample Interview Questions on Recommender Systems
    6. Reinforcement Learning Algorithms
      1. Summarizing Reinforcement Learning Agents
      2. Summarizing Q-Learning
      3. Summarizing Model-Based Versus Model-Free Reinforcement Learning
      4. Summarizing Value-Based Versus Policy-Based Reinforcement Learning
      5. Summarizing On-Policy Versus Off-Policy Reinforcement Learning
      6. Sample Interview Questions on Reinforcement Learning
    7. Computer Vision Algorithms
      1. Summarizing Common Image Datasets
      2. Summarizing Convolutional Neural Networks (CNNs)
      3. Summarizing Transfer Learning
      4. Summarizing Generative Adversarial Networks
      5. Summarizing Additional Computer Vision Use Cases
      6. Sample Interview Questions on Image Recognition
    8. Summary
  5. 4. Technical Interview: Model Training and Evaluation
    1. Defining a Machine Learning Problem
    2. Data Preprocessing and Feature Engineering
      1. Introduction to Data Acquisition
      2. Introduction to Exploratory Data Analysis
      3. Introduction to Feature Engineering
      4. Sample Interview Questions on Data Preprocessing and Feature Engineering
    3. The Model Training Process
      1. The Iteration Process in Model Training
      2. Defining the ML Task
      3. Overview of Model Selection
      4. Overview of Model Training
      5. Sample Interview Questions on Model Selection and Training
    4. Model Evaluation
      1. Summary of Common ML Evaluation Metrics
      2. Trade-offs in Evaluation Metrics
      3. Additional Methods for Offline Evaluation
      4. Model Versioning
      5. Sample Interview Questions on Model Evaluation
    5. Summary
  6. 5. Technical Interview: Coding
    1. Starting from Scratch: Learning Roadmap If You Don’t Know Python
      1. Pick Up a Book or Course That’s Easy to Understand
      2. Start with Easy Questions on LeetCode, HackerRank, or Your Platform of Choice
      3. Set a Measurable Target and Practice, Practice, Practice
      4. Try Out ML-Related Python Packages
    2. Coding Interview Success Tips
      1. Think Out Loud
      2. Control the Flow
      3. Your Interviewer Can Help You Out
      4. Optimize Your Environment
      5. Interviews Require Energy!
    3. Python Coding Interview: Data- and ML-Related Questions
      1. Sample Data- and ML-Related Interview and Questions
      2. FAQs for Data- and ML-Focused Interviews
      3. Resources for Data and ML Interview Questions
    4. Python Coding Interview: Brainteaser Questions
      1. Patterns for Brainteaser Programming Questions
      2. Resources for Brainteaser Programming Questions
    5. SQL Coding Interview: Data-Related Questions
      1. Resources for SQL Coding Interview Questions
    6. Roadmaps for Preparing for Coding Interviews
      1. Coding Interview Roadmap Example: Four Weeks, University Student
      2. Coding Interview Roadmap Example: Six Months, Career Transition
      3. Coding Interview Roadmap: Create Your Own!
    7. Summary
  7. 6. Technical Interview: Model Deployment and End-to-End ML
    1. Model Deployment
      1. The Main Experience Gap for New Entrants into the ML Industry
      2. Should Data Scientists and MLEs Know This?
      3. End-to-End Machine Learning
      4. Cloud Environments and Local Environments
      5. Overview of Model Deployment
      6. Additional Tooling to Know
      7. On-Device Machine Learning
      8. Interviews for Roles Focused on Model Training
    2. Model Monitoring
      1. Monitoring Setups
      2. ML-Related Monitoring Metrics
    3. Overview of Cloud Providers
      1. GCP
      2. AWS
      3. Microsoft Azure
    4. Developer Best Practices for Interviews
      1. Version Control
      2. Dependency Management
      3. Code Review
      4. Tests
    5. Additional Technical Interview Components
      1. Machine Learning Systems Design Interview
      2. Technical Deep-Dive Interview
      3. Take-Home Exercise Tips
      4. Product Sense
      5. Sample Interview Questions on MLOps
    6. Summary
  8. 7. Behavioral Interviews
    1. Behavioral Interview Questions and Responses
      1. Use the STAR Method to Answer Behavioral Questions
      2. Enhance Your Answers with the Hero’s Journey Method
      3. Best Practices and Feedback from an Interviewer’s Perspective
    2. Common Behavioral Questions and Recommendations
      1. Questions About Communication Skills
      2. Questions About Collaboration and Teamwork
      3. Questions on How You Respond to Feedback
      4. Questions on Dealing with Challenges and Learning New Skills
      5. Questions About the Company
      6. Questions About Work Projects
      7. Free-Form Questions
    3. Behavioral Interview Best Practices
      1. How to Answer Behavioral Questions If You Don’t Have Relevant Work Experience
      2. Senior+ Behavioral Interview Tips
    4. Specific Preparation Examples for Big Tech
      1. Amazon
      2. Meta/Facebook
      3. Alphabet/Google
      4. Netflix
    5. Summary
  9. 8. Tying It All Together: Your Interview Roadmap
    1. Interview Preparation Checklist
    2. Interview Roadmap Template
    3. Efficient Interview Preparation
      1. Become a Better Learner
      2. Time Management and Accountability
      3. Avoid Burnout: It Is Costly
    4. Impostor Syndrome
    5. Summary
  10. 9. Post-Interview and Follow-up
    1. Post-Interview Steps
      1. Take Notes of What You Remember from the Interview
      2. Make Sure You’re Not Missing Important Information
      3. Should You Send a Thank-You Email to the Interviewer?
      4. Thank-You Note Template
      5. How Long Should You Wait After the Interview for a Response Before Following Up?
    2. What to Do Between Interviews
      1. How to Respond to Rejections
      2. Template for Rejection Responses
      3. Job Applications Are a Funnel
      4. Update and Customize Your Resume and Test Variations
    3. Steps of the Offer Stage
      1. Let Other Interviews-in-Progress Know You’ve Gotten an Offer
      2. What to Do If the Offer Response Timeline Is Very Short
      3. Understand Your Offer
    4. First 30/60/90 Days of Your New ML Job
      1. Gain Domain Knowledge
      2. Gain Code Knowledge
      3. Meet Relevant People
      4. Help Improve the Onboarding Documentation
      5. Keep Track of Your Achievements
    5. Summary
  11. Epilogue
  12. Index
  13. About the Author

Product information

  • Title: Machine Learning Interviews
  • Author(s): Susan Shu Chang
  • Release date: December 2023
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098146542