Machine Learning Interviews Kickstart Your Machine Learning and Data Career (Susan Shu Chang) (Z-Library)

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Susan Shu Chang Machine Learning Interviews Kickstart Your Machine Learning and Data Career

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DATA “A one-stop book for everything related to ML interviews. This book will prove useful for those new to the field, and also for experienced ML practitioners and data scientists to cover the content in most ML interviews.” —Prithvishankar Srinivasan ML Engineer, Instacart (formerly at Twitter, Microsoft) Machine Learning Interviews Twitter: @oreillymedia linkedin.com/company/oreilly-media youtube.com/oreillymedia As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skillsets 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’s 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 skills necessary for each ML role and craft an application-ready resume • Ace ML interview topics, including coding assessments, statistics and ML theory, and behavioral questions • Prepare for these interviews by studying common ML interview patterns and questions • Get post-interview tips and other valuable resources Susan Shu Chang is a principal data scientist at Elastic (of Elasticsearch), with previous ML experience in fintech, telecommunications, and social platforms. She’s an international speaker, having given talks at six PyCons worldwide and keynotes at Data Day Texas, PyCon DE & PyData Berlin, and O’Reilly’s AI Superstream. She writes about machine learning career growth in her newsletter, susanshu.substack.com. 9 7 8 1 0 9 8 1 4 6 5 4 2 5 7 9 9 9 US $79.99 CAN $99.99 ISBN: 978-1-098-14654-2

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Susan Shu Chang Machine Learning Interviews Kickstart Your Machine Learning and Data Career Boston Farnham Sebastopol TokyoBeijing

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978-1-098-14654-2 [LSI] Machine Learning Interviews by Susan Shu Chang Copyright © 2024 Quill Game Studios Inc. o/a Quill Technologies. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institu‐ tional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Nicole Butterfield Indexer: Sue Klefstad Development Editor: Sara Hunter Interior Designer: David Futato Production Editor: Beth Kelly Cover Designer: Karen Montgomery Copyeditor: Shannon Turlington Illustrator: Kate Dullea Proofreader: Piper Editorial Consulting, LLC December 2023: First Edition Revision History for the First Edition 2023-11-29: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098146542 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Machine Learning Interviews, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 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 6 Machine Learning Lifecycle 8 Startups 9 Larger ML Teams 10 The Three Pillars of Machine Learning Roles 12 Machine Learning Algorithms and Data Intuition: Ability to Adapt 12 Programming and Software Engineering: Ability to Build 13 Execution and Communication: Ability to Get Things Done in a Team 13 Clearing Minimum Requirements in the Three ML Pillars 14 Machine Learning Skills Matrix 15 Introduction to ML Job Interviews 17 Machine Learning Job-Interview Process 18 Applying for Jobs Through Websites or Job Boards 19 Resume Screening of Website or Job-Board Applications 20 Applying via a Referral 22 Preinterview Checklist 23 Recruiter Screening 25 Overview of Main Interview Loop 26 Summary 28 2. Machine Learning Job Application and Resume. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Where Are the Jobs? 29 ML Job Application Guide 30 iii

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Your Effectiveness per Application 30 Job Referrals 31 Networking 35 Machine Learning Resume Guide 37 Take Inventory of Your Past Experience 37 Overview of Resume Sections 39 Tailoring Your Resume to Your Desired Role(s) 44 Final Resume Touch-ups 48 Applying to Jobs 49 Vetting Job Postings 49 Mapping Your Skills and Experience to the ML Skills Matrix 49 Tracking Applications 51 Additional Job Application Materials, Credentials, and FAQ 52 Do You Need a Project Portfolio? 52 Do Online Certifications Help? 53 FAQ: How Many Pages Should My Resume Be? 56 FAQ: Should I Format My Resume for ATS (Applicant Tracking Systems)? 57 Next Steps 58 Browsing Job Postings 58 Identifying the Gaps Between Your Current Skills and Target Roles 58 Summary 61 3. Technical Interview: Machine Learning Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Overview of the Machine Learning Algorithms Technical Interview 63 Statistical and Foundational Techniques 65 Summarizing Independent and Dependent Variables 66 Defining Models 67 Summarizing Linear Regression 68 Defining Training and Test Set Splits 71 Defining Model Underfitting and Overfitting 72 Summarizing Regularization 73 Sample Interview Questions on Foundational Techniques 74 Supervised, Unsupervised, and Reinforcement Learning 76 Defining Labeled Data 77 Summarizing Supervised Learning 78 Defining Unsupervised Learning 78 Summarizing Semisupervised and Self-Supervised Learning 79 Summarizing Reinforcement Learning 81 Sample Interview Questions on Supervised and Unsupervised Learning 81 Natural Language Processing Algorithms 86 Summarizing NLP Underlying Concepts 87 Summarizing Long Short-Term Memory Networks 88 iv | Table of Contents

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Summarizing Transformer Models 89 Summarizing BERT Models 89 Summarizing GPT Models 91 Going Further 91 Sample Interview Questions on NLP 92 Recommender System Algorithms 95 Summarizing Collaborative Filtering 95 Summarizing Explicit and Implicit Ratings 96 Summarizing Content-Based Recommender Systems 96 User-Based/Item-Based Versus Content-Based Recommender Systems 97 Summarizing Matrix Factorization 97 Sample Interview Questions on Recommender Systems 98 Reinforcement Learning Algorithms 101 Summarizing Reinforcement Learning Agents 102 Summarizing Q-Learning 104 Summarizing Model-Based Versus Model-Free Reinforcement Learning 106 Summarizing Value-Based Versus Policy-Based Reinforcement Learning 107 Summarizing On-Policy Versus Off-Policy Reinforcement Learning 108 Sample Interview Questions on Reinforcement Learning 108 Computer Vision Algorithms 111 Summarizing Common Image Datasets 112 Summarizing Convolutional Neural Networks (CNNs) 113 Summarizing Transfer Learning 114 Summarizing Generative Adversarial Networks 114 Summarizing Additional Computer Vision Use Cases 116 Sample Interview Questions on Image Recognition 118 Summary 119 4. Technical Interview: Model Training and Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Defining a Machine Learning Problem 122 Data Preprocessing and Feature Engineering 124 Introduction to Data Acquisition 124 Introduction to Exploratory Data Analysis 125 Introduction to Feature Engineering 126 Sample Interview Questions on Data Preprocessing and Feature Engineering 132 The Model Training Process 133 The Iteration Process in Model Training 133 Defining the ML Task 135 Overview of Model Selection 136 Overview of Model Training 138 Sample Interview Questions on Model Selection and Training 140 Table of Contents | v

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Model Evaluation 141 Summary of Common ML Evaluation Metrics 142 Trade-offs in Evaluation Metrics 145 Additional Methods for Offline Evaluation 146 Model Versioning 147 Sample Interview Questions on Model Evaluation 148 Summary 149 5. Technical Interview: Coding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Starting from Scratch: Learning Roadmap If You Don’t Know Python 152 Pick Up a Book or Course That’s Easy to Understand 153 Start with Easy Questions on LeetCode, HackerRank, or Your Platform of Choice 153 Set a Measurable Target and Practice, Practice, Practice 154 Try Out ML-Related Python Packages 154 Coding Interview Success Tips 154 Think Out Loud 154 Control the Flow 155 Your Interviewer Can Help You Out 156 Optimize Your Environment 157 Interviews Require Energy! 157 Python Coding Interview: Data- and ML-Related Questions 158 Sample Data- and ML-Related Interview and Questions 158 FAQs for Data- and ML-Focused Interviews 166 Resources for Data and ML Interview Questions 167 Python Coding Interview: Brainteaser Questions 168 Patterns for Brainteaser Programming Questions 169 Resources for Brainteaser Programming Questions 177 SQL Coding Interview: Data-Related Questions 178 Resources for SQL Coding Interview Questions 180 Roadmaps for Preparing for Coding Interviews 180 Coding Interview Roadmap Example: Four Weeks, University Student 181 Coding Interview Roadmap Example: Six Months, Career Transition 183 Coding Interview Roadmap: Create Your Own! 184 Summary 184 6. Technical Interview: Model Deployment and End-to-End ML. . . . . . . . . . . . . . . . . . . . . 185 Model Deployment 186 The Main Experience Gap for New Entrants into the ML Industry 186 Should Data Scientists and MLEs Know This? 188 End-to-End Machine Learning 189 Cloud Environments and Local Environments 191 vi | Table of Contents

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Overview of Model Deployment 194 Additional Tooling to Know 197 On-Device Machine Learning 198 Interviews for Roles Focused on Model Training 198 Model Monitoring 200 Monitoring Setups 200 ML-Related Monitoring Metrics 203 Overview of Cloud Providers 203 GCP 204 AWS 205 Microsoft Azure 206 Developer Best Practices for Interviews 206 Version Control 207 Dependency Management 208 Code Review 208 Tests 209 Additional Technical Interview Components 209 Machine Learning Systems Design Interview 210 Technical Deep-Dive Interview 213 Take-Home Exercise Tips 214 Product Sense 214 Sample Interview Questions on MLOps 215 Summary 217 7. Behavioral Interviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Behavioral Interview Questions and Responses 220 Use the STAR Method to Answer Behavioral Questions 221 Enhance Your Answers with the Hero’s Journey Method 222 Best Practices and Feedback from an Interviewer’s Perspective 225 Common Behavioral Questions and Recommendations 227 Questions About Communication Skills 227 Questions About Collaboration and Teamwork 228 Questions on How You Respond to Feedback 229 Questions on Dealing with Challenges and Learning New Skills 229 Questions About the Company 230 Questions About Work Projects 230 Free-Form Questions 231 Behavioral Interview Best Practices 231 How to Answer Behavioral Questions If You Don’t Have Relevant Work Experience 232 Senior+ Behavioral Interview Tips 233 Specific Preparation Examples for Big Tech 235 Table of Contents | vii

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Amazon 235 Meta/Facebook 236 Alphabet/Google 237 Netflix 238 Summary 239 8. Tying It All Together: Your Interview Roadmap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Interview Preparation Checklist 241 Interview Roadmap Template 242 Efficient Interview Preparation 244 Become a Better Learner 244 Time Management and Accountability 246 Avoid Burnout: It Is Costly 248 Impostor Syndrome 249 Summary 250 9. Post-Interview and Follow-up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Post-Interview Steps 251 Take Notes of What You Remember from the Interview 252 Make Sure You’re Not Missing Important Information 252 Should You Send a Thank-You Email to the Interviewer? 252 Thank-You Note Template 252 How Long Should You Wait After the Interview for a Response Before Following Up? 254 What to Do Between Interviews 254 How to Respond to Rejections 254 Template for Rejection Responses 254 Job Applications Are a Funnel 255 Update and Customize Your Resume and Test Variations 256 Steps of the Offer Stage 257 Let Other Interviews-in-Progress Know You’ve Gotten an Offer 257 What to Do If the Offer Response Timeline Is Very Short 257 Understand Your Offer 258 First 30/60/90 Days of Your New ML Job 261 Gain Domain Knowledge 262 Gain Code Knowledge 262 Meet Relevant People 263 Help Improve the Onboarding Documentation 263 Keep Track of Your Achievements 263 Summary 264 viii | Table of Contents

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Epilogue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Table of Contents | ix

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Preface Machine learning (ML) is an integral part of our day to day, whether we’re aware of it or not. Each time you go on sites like YouTube and Amazon.com, you’re interacting with ML, which powers personalized recommendations. This means that the way the products are displayed on the sites is based on what ML algorithms think suit your taste and interests. And not just that—there’s ML-based comment moderation to flag spam or toxic comments, review moderation, and more. On sites like YouTube, there are ML-generated captions and translations. ML is also present in aspects of our lives beyond shopping and entertainment. For example, when you send a money transfer online, ML algorithms are checking to see whether it’s fraudulent. We live in an age of software that is built on a foundation of data and ML algorithms. All of this software requires specialized talent to design and build, which has created a demand for software skills and has elevated ML careers in recent years. The pay for technology roles has also risen as a result. These are just some of the many factors that make an ML career enticing: building the products and product features that are so integral to our lives. Since ML techniques power AI advancements, this discussion similarly applies to “AI careers.” Entering the ML field is challenging, however. ML jobs have a reputation for requir‐ ing higher academic credentials, with most of the jobs in the 2010s requiring a PhD. Even if the credential requirements on job postings have decreased since the late 2010s, the advice I still commonly see online is to have at least a master’s degree. Even those with ample credentials can struggle to find a role in the data and ML fields. Is the advice given online wrong, or is it too generalized and vague? xi

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1 Staff+ refers to roles that are above the senior level. 2 The job levels in tech often progress from entry/intermediate level → senior level → staff level → principal level, although there are minor differences depending on the company. For example, some companies com‐ bine the staff and principal levels. 3 Randy Au, “Old Dog Revisits the DS Job Market out of Curiosity,” Counting Stuff (blog), December 1, 2022, https://oreil.ly/yzIsx. I’ve interviewed for numerous ML jobs, and I’ve been successful at entry level, senior level, and the staff+1 and principal2 levels. Throughout the process, I’ve experienced firsthand the same difficulties and frustrations that aspiring candidates encounter during ML interviews. I’ve sent out endless resumes only to get no replies. I’ve failed phone screens, suffered the anxiety of waiting for responses, and even failed an on- site after they’d flown me to San Francisco from Toronto. I’ve applied for data scien‐ tist and machine learning engineer (MLE) jobs only to be confused when the interviewers seemed to be looking more for a data engineer or data analyst. Apart from my experience as an interviewee, I’ve built years of experience as an inter‐ viewer. As part of my jobs in the ML field, I’ve reviewed and filtered hundreds of resumes, conducted numerous interviews, and served on many decision-making committees. As part of technical leadership (principal level at two companies), I’ve reviewed job descriptions and interviewed co-ops, interns, and entry-level candidates as well as senior and staff+ hires. I’ve included tips in this book based on mistakes made by job candidates that resulted in my fellow interviewers and me deciding not to pass them on to the next round. “If only the candidate had done this other thing,” we said. “They were quite promising otherwise.” This book will help you avoid some of these obvious mistakes. The truth is that there are a lot of unspoken criteria for job seekers. For example, hav‐ ing good communication and teamwork skills may not be included in some job descriptions. Expectations such as these aren’t omitted from job descriptions because of malice but because those in the industry see them as minimum requirements. I have more recently seen ML job postings from major companies clearly list “commu‐ nication skills” at the very top of their lists of requirements in an attempt to improve the clarity of job descriptions. In addition to these hidden expectations for new and experienced job seekers alike, the interview process can be confusing because it differs so much from role to role and from company to company. Even Randy Au, a writer who’s worked in data at Google for years, said that “things are … different”3 when, out of curiosity, he looked at current data scientist and ML job postings. Many people wish for a roadmap, a full step-by-step for how to enter the ML field, guaranteed. For example, what are the best university majors and internships? What are the best side projects, and what Python libraries should you learn? I can relate to xii | Preface

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4 Science, technology, engineering, and mathematics. this—I’ve asked many friends for as much information as possible along each step of my job interview journey. I worried about whether I should send a follow-up email after an interview and looked in multiple online forums to see if I should. Would I annoy the interviewers, or would they be expecting it? Such a small thing caused me a lot of anxiety, and I wished there were just a clear answer instead of “it depends” or “it probably won’t hurt.” This is the book I wish I’d had back then to refer to for all those questions! Now that I’ve been on the other side as an interviewer, I’ve learned what the hiring side prefers in job candidates in various scenarios. I now have firsthand answers to many questions I had in the past, and more of a roadmap to entering the ML field. Although even if there were such a guaranteed roadmap, it won’t be the one you are imagining. By the time I learned about the ML and data science fields, I had long ago chosen my university major, graduated, and was partway through a master’s degree in economics. I didn’t have any internships during university; instead, I made and played video games and socialized in my spare time. If anything, the roadmap to an ML job is quite flexible, and even if you start a bit later, there is no such thing as being too late. When I was searching for my first ML job, I didn’t do all the most straightforward things, but I was somehow able to make my way through job interviews as a student who had never done an internship. I probably knew less about the interview process than many people did, but that’s why I’ve been able to write from a perspective of someone who didn’t do all the right things and was still able to thrive in the ML field. Indeed, there are no right things, only the things that are right for your situation. I won’t tell you things like, “Just major in [SUBJECT] at your university and then get an internship at [COMPANY], and you’ll be set.” I’d need to write a separate book for each different type of person. A one-size-fits-all, prescriptive roadmap will fail when you encounter a point not already on the map. If you learn how to navigate without being glued to a map, you can create your own maps, regardless of the situation. In this book, I’ll show you how to be a navigator and create your own roadmap, whether you are a non-STEM4 major, a STEM major without internship experience, have no relevant work experience, have ML work experience or non-ML work expe‐ rience, and so on. As long as you stick with it, it’ll be fine if you majored in something that’s not often recommended. It’s OK if you have previous job experience that you don’t think is directly relevant to ML. I’ll walk you through how to enhance and make use of your past experiences as well as how to gain additional relevant experience. Preface | xiii

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5 A public company means it has publicly traded stocks. I advocate for flexible and tailored career roadmaps based on your own scenario because in my own career I’ve encountered many scenarios in which there wasn’t one single roadmap: • Landing an entry-level data scientist (ML) job as an economics master’s degree student at a large, public company5 • Landing a job with a more senior role at a startup with about 200 employees when I joined, and about 400 employees at peak • Landing a job at a new, mid-large public company as a principal data scientist Depending on the industry, the company size, the ML team size, and the company’s lifecycle stage (e.g., startup), employers had different expectations that I needed to learn about. If I had only followed online advice or advice from people who inter‐ viewed at companies that used a different job-interview process, I might have failed (no, I would have failed). Each time, I’ve had to change up the way I prepare and the way I interview in order to succeed. Through all my personal experiences and (liter‐ ally) hundreds of ML interviews, I’ve found patterns for how to ace ML and data sci‐ ence job interviews and be a successful candidate. With my experiences and the lessons I’ve learned, it’s now possible to write this book to help aspiring job candidates. Successful job candidates know what each step in the interview process is trying to assess in their scenario. Unfortunately, simply showing up and having the technical skills isn’t always enough. It’s like exams at school—people who look at the syllabus carefully and understand the scope of each exam are more likely to succeed. In this case, you try to reverse engineer a syllabus for each of the jobs you are applying for. As I gained more and more experience in ML, I also got more and more questions from aspiring job seekers. I’ve taken on many coffee chats (100+ at this point) and to help even more people, I’ve written career guides for my blog susanshu.com for years. When the opportunity to help even more people with this book came up, the decision for me was clear. Why Machine Learning Jobs? I’ve spoken about how ML is prevalent in our day-to-day lives, whether we know it or not, and whether we like it or not. You may have had some experiences in your own life that caused you to become curious and pick up this book! I’ll also outline my experiences, which may reinforce your motivations or bring even more attractive aspects of the ML field to your attention. xiv | Preface

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As someone working in tech, I think ML is a great area to develop high-value prod‐ ucts that can affect millions of users. I had the chance to work on such a project in my very first job out of school, and I think that I might not have had that responsibil‐ ity and opportunity so early in my career if I hadn’t been skilled in machine learning. In my opinion, ML is a fun and fulfilling area. I enjoy learning about new technolo‐ gies and research, and if you relate to that, you’ll enjoy that facet of working in ML too. There is a flip side to the fast-paced innovations in our field. For example, it can be exhausting to continuously learn about new advancements when trying to focus on family or other important aspects of our lives. Nowadays, even if I’m very focused on other activities such as socializing or writing this book on the weekends, I still take the opportunity to learn without spending too much time. I also take some time dur‐ ing work hours to listen to talks online or read books. This isn’t exclusive to ML, but I’ve heard from many people that the pace of continuous learning for ML is a bit faster than for other tech-related jobs that require learning new frameworks. Of course, there is also the aspect of pay. On average, ML jobs are well compensated. I’ve been able to provide for myself and even accomplish many financial goals that enhance my life and the lives of my loved ones. This is something I’m very grateful to my ML career for enabling. On another note, I’ve been able to achieve so much because of the ML field and community: I’ve been flown around the world to speak at conferences (so many of them that I’ve had to rain check for future years). Meeting cool people working at cool places in ML and seeing advancements in the ML and AI space firsthand are all perks of working in this industry. No matter what your motivation for picking up this book is, I hope that I can suc‐ cessfully share with you the skills and tools for you to succeed at ML job interviews and to overcome roadblocks along the way. In this book I’ll help you understand the following: • The various types of ML roles and which ones you’d be most likely to succeed at • The building blocks of ML interviews • How to identify your skill gaps and target your interview preparation efficiently • How to succeed at both technical and behavioral interviews I’ll be adding commonly asked questions from the online live training I’ve taught at O’Reilly as well. Consider it a coffee chat with me and the various sources I’ve gained supporting insights from: • How to succeed as a candidate with a less “typical” educational or career background • How to greatly increase the chances that your resume will clear the initial screening Preface | xv

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• What ML interviews for senior and higher roles look like And more. Who This Book Is For Before I dive into the chapters, I want to outline the following scenarios that you might find relatable; this is the audience I’ve written this book for: • You are a recent graduate who is eager to become an ML/AI practitioner in industry. • You are a software engineer, data analyst, or other tech/data professional who is transitioning into a role that focuses on ML day to day. • You are a professional with experience in another field who is interested in tran‐ sitioning into the ML field. • You are an experienced data scientist or ML practitioner who is returning to the interviewing fray and aiming for a different role or an increased title and respon‐ sibility, and you would like a comprehensive refresher of ML material. You could also benefit from this book if the following scenarios describe you: • Managers who want to get inspiration for how to conduct their ML interviews or nontechnical people who want to get an overview of the process without wasting too much time on scattered online resources • Readers who have a basic knowledge of Python programming and ML theory and are curious to explore if entering the ML field could be a future career choice What This Book Is Not • This book is not a statistics or ML textbook. • This book is not a coding textbook or tutorial book. • While there are sample interview questions, this book is not a question bank. Code snippets will be brief and concise since they become outdated quickly. Since I can’t cover every concept from scratch, I assume that readers have a rudimen‐ tary familiarity with ML (a high-level understanding is enough). But don’t worry, as I will cover the basic definitions as a quick reminder. I also assume the audience has some familiarity with the Python programming language, such as running scripts on Jupyter Notebooks, since Python is popular in ML interviews and on the job. How‐ ever, I do include a brief section on learning Python from scratch if you happen to not be familiar with it. xvi | Preface

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In addition, this book provides a substantial library of links to external practice resources to help you with preparing for ML interviews; but first, I’ll help you iden‐ tify what is most helpful for you to practice and learn beyond your current knowl‐ edge and skill level. Thus, instead of listing a bunch of questions and answers to memorize, with this book I’m aiming to teach you how to fish. As an interviewer, many candidates I’ve seen who didn’t pass the interview wouldn’t have been saved if they had just practiced some more questions. Rather, they didn’t even know what their gaps were. I’ll teach you how to identify your strengths and gaps and how exactly you can use the resources in this book to close those gaps. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. Preface | xvii

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This element indicates a warning or caution. O’Reilly Online Learning For more than 40 years, O’Reilly Media has provided technol‐ ogy and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit https://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-889-8969 (in the United States or Canada) 707-829-7019 (international or local) 707-829-0104 (fax) support@oreilly.com https://www.oreilly.com/about/contact.html We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at https://oreil.ly/ML-interviews. There is a supplemental website that includes bonus content not found in the book: https://susanshu.substack.com. For news and information about our books and courses, visit https://oreilly.com. Find us on LinkedIn: https://linkedin.com/company/oreilly-media Follow us on Twitter: https://twitter.com/oreillymedia Watch us on YouTube: https://youtube.com/oreillymedia xviii | Preface