book

Machine Learning Interviews

Name: Machine Learning Interviews
Author: Susan Shu Chang
ISBN: 9781098146542

by Susan Shu Chang

November 2023

Beginner to intermediate

310 pages

9h 5m

English

O'Reilly Media, Inc.

Audiobook available

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Includes

Quizzes

Preface
Why Machine Learning Jobs?Who This Book Is ForWhat This Book Is NotConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
1. Machine Learning Roles and the Interview Process
Overview of This BookA Brief History of Machine Learning and Data Science Job TitlesJob Titles Requiring ML ExperienceMachine Learning LifecycleStartupsLarger ML TeamsThe Three Pillars of Machine Learning RolesMachine Learning Algorithms and Data Intuition: Ability to AdaptProgramming and Software Engineering: Ability to BuildExecution and Communication: Ability to Get Things Done in a TeamClearing Minimum Requirements in the Three ML PillarsMachine Learning Skills MatrixIntroduction to ML Job InterviewsMachine Learning Job-Interview ProcessApplying for Jobs Through Websites or Job BoardsResume Screening of Website or Job-Board ApplicationsApplying via a ReferralPreinterview ChecklistRecruiter ScreeningOverview of Main Interview LoopSummary
2. Machine Learning Job Application and Resume
Where Are the Jobs?ML Job Application GuideYour Effectiveness per ApplicationJob ReferralsNetworkingMachine Learning Resume GuideTake Inventory of Your Past ExperienceOverview of Resume SectionsTailoring Your Resume to Your Desired Role(s)Final Resume Touch-upsApplying to JobsVetting Job PostingsMapping Your Skills and Experience to the ML Skills MatrixTracking ApplicationsAdditional Job Application Materials, Credentials, and FAQDo You Need a Project Portfolio?Do Online Certifications Help?FAQ: How Many Pages Should My Resume Be?FAQ: Should I Format My Resume for ATS (Applicant Tracking Systems)?Next StepsBrowsing Job PostingsIdentifying the Gaps Between Your Current Skills and Target RolesSummary
3. Technical Interview: Machine Learning Algorithms
Overview of the Machine Learning Algorithms Technical InterviewStatistical and Foundational TechniquesSummarizing Independent and Dependent VariablesDefining ModelsSummarizing Linear RegressionDefining Training and Test Set SplitsDefining Model Underfitting and OverfittingSummarizing RegularizationSample Interview Questions on Foundational TechniquesSupervised, Unsupervised, and Reinforcement LearningDefining Labeled DataSummarizing Supervised LearningDefining Unsupervised LearningSummarizing Semisupervised and Self-Supervised LearningSummarizing Reinforcement LearningSample Interview Questions on Supervised and Unsupervised LearningNatural Language Processing AlgorithmsSummarizing NLP Underlying ConceptsSummarizing Long Short-Term Memory NetworksSummarizing Transformer ModelsSummarizing BERT ModelsSummarizing GPT ModelsGoing FurtherSample Interview Questions on NLPRecommender System AlgorithmsSummarizing Collaborative FilteringSummarizing Explicit and Implicit RatingsSummarizing Content-Based Recommender SystemsUser-Based/Item-Based Versus Content-Based Recommender SystemsSummarizing Matrix FactorizationSample Interview Questions on Recommender SystemsReinforcement Learning AlgorithmsSummarizing Reinforcement Learning AgentsSummarizing Q-LearningSummarizing Model-Based Versus Model-Free Reinforcement LearningSummarizing Value-Based Versus Policy-Based Reinforcement LearningSummarizing On-Policy Versus Off-Policy Reinforcement LearningSample Interview Questions on Reinforcement LearningComputer Vision AlgorithmsSummarizing Common Image DatasetsSummarizing Convolutional Neural Networks (CNNs)Summarizing Transfer LearningSummarizing Generative Adversarial NetworksSummarizing Additional Computer Vision Use CasesSample Interview Questions on Image RecognitionSummary
4. Technical Interview: Model Training and Evaluation
Defining a Machine Learning ProblemData Preprocessing and Feature EngineeringIntroduction to Data AcquisitionIntroduction to Exploratory Data AnalysisIntroduction to Feature EngineeringSample Interview Questions on Data Preprocessing and Feature EngineeringThe Model Training ProcessThe Iteration Process in Model TrainingDefining the ML TaskOverview of Model SelectionOverview of Model TrainingSample Interview Questions on Model Selection and TrainingModel EvaluationSummary of Common ML Evaluation MetricsTrade-offs in Evaluation MetricsAdditional Methods for Offline EvaluationModel VersioningSample Interview Questions on Model EvaluationSummary
5. Technical Interview: Coding
Starting from Scratch: Learning Roadmap If You Don’t Know PythonPick Up a Book or Course That’s Easy to UnderstandStart with Easy Questions on LeetCode, HackerRank, or Your Platform of ChoiceSet a Measurable Target and Practice, Practice, PracticeTry Out ML-Related Python PackagesCoding Interview Success TipsThink Out LoudControl the FlowYour Interviewer Can Help You OutOptimize Your EnvironmentInterviews Require Energy!Python Coding Interview: Data- and ML-Related QuestionsSample Data- and ML-Related Interview and QuestionsFAQs for Data- and ML-Focused InterviewsResources for Data and ML Interview QuestionsPython Coding Interview: Brainteaser QuestionsPatterns for Brainteaser Programming QuestionsResources for Brainteaser Programming QuestionsSQL Coding Interview: Data-Related QuestionsResources for SQL Coding Interview QuestionsRoadmaps for Preparing for Coding InterviewsCoding Interview Roadmap Example: Four Weeks, University StudentCoding Interview Roadmap Example: Six Months, Career TransitionCoding Interview Roadmap: Create Your Own!Summary
6. Technical Interview: Model Deployment and End-to-End ML
Model DeploymentThe Main Experience Gap for New Entrants into the ML IndustryShould Data Scientists and MLEs Know This?End-to-End Machine LearningCloud Environments and Local EnvironmentsOverview of Model DeploymentAdditional Tooling to KnowOn-Device Machine LearningInterviews for Roles Focused on Model TrainingModel MonitoringMonitoring SetupsML-Related Monitoring MetricsOverview of Cloud ProvidersGCPAWSMicrosoft AzureDeveloper Best Practices for InterviewsVersion ControlDependency ManagementCode ReviewTestsAdditional Technical Interview ComponentsMachine Learning Systems Design InterviewTechnical Deep-Dive InterviewTake-Home Exercise TipsProduct SenseSample Interview Questions on MLOpsSummary
7. Behavioral Interviews
Behavioral Interview Questions and ResponsesUse the STAR Method to Answer Behavioral QuestionsEnhance Your Answers with the Hero’s Journey MethodBest Practices and Feedback from an Interviewer’s PerspectiveCommon Behavioral Questions and RecommendationsQuestions About Communication SkillsQuestions About Collaboration and TeamworkQuestions on How You Respond to FeedbackQuestions on Dealing with Challenges and Learning New SkillsQuestions About the CompanyQuestions About Work ProjectsFree-Form QuestionsBehavioral Interview Best PracticesHow to Answer Behavioral Questions If You Don’t Have Relevant Work ExperienceSenior+ Behavioral Interview TipsSpecific Preparation Examples for Big TechAmazonMeta/FacebookAlphabet/GoogleNetflixSummary
8. Tying It All Together: Your Interview Roadmap
Interview Preparation ChecklistInterview Roadmap TemplateEfficient Interview PreparationBecome a Better LearnerTime Management and AccountabilityAvoid Burnout: It Is CostlyImpostor SyndromeSummary
9. Post-Interview and Follow-up
Post-Interview StepsTake Notes of What You Remember from the InterviewMake Sure You’re Not Missing Important InformationShould You Send a Thank-You Email to the Interviewer?Thank-You Note TemplateHow Long Should You Wait After the Interview for a Response Before Following Up?What to Do Between InterviewsHow to Respond to RejectionsTemplate for Rejection ResponsesJob Applications Are a FunnelUpdate and Customize Your Resume and Test VariationsSteps of the Offer StageLet Other Interviews-in-Progress Know You’ve Gotten an OfferWhat to Do If the Offer Response Timeline Is Very ShortUnderstand Your OfferFirst 30/60/90 Days of Your New ML JobGain Domain KnowledgeGain Code KnowledgeMeet Relevant PeopleHelp Improve the Onboarding DocumentationKeep Track of Your AchievementsSummary

Epilogue
Index
About the Author

Content preview from Machine Learning Interviews

Chapter 9. Post-Interview and Follow-up

Since you’ve read to this point in the book, I hope you can apply what you have learned to your job search! It can be a game of persistence: while interviewing for one of my past roles, I applied to around 70 openings, and I got 10 recruiter interviews and two final-round interviews, which led to an offer. The interviews spanned several months, during which I kept running into new scenarios and questions.

You might have the same questions along your interviewing journey. Let’s say you’ve gotten some interviews, and you’re eagerly awaiting their response. What should you do at this stage? Of course, the ideal outcome is that you can smoothly move to the next stage, but statistically speaking, there will be times where you hear the unfortunate news that the hiring team has moved on with other candidates. What should you do in this situation?

In this chapter, I will go through the final steps, from post-interview to offer stage, covering FAQs that I’ve gotten from job candidates. I will lay out some scenarios that I’ve personally dealt with and that many job candidates have dealt with, too, and I will share advice to arrive at the best outcome. I’ll also go through some tips for your first 30/60/90 days at a new ML job, to set yourself and your ML career up for success!

Post-Interview Steps

After the interview, I’m often just ready to relax and forget about it. But here are some actions I try to do (most of the time) after each interview to ...

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Machine Learning Interviews

by Susan Shu Chang

Chapter 9. Post-Interview and Follow-up

Post-Interview Steps

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