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Spis treści

Machine Learning Interviews eBook -- spis treści

• Preface
  • Why Machine Learning Jobs?
  • Who This Book Is For
  • What This Book Is Not
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Machine Learning Roles and the Interview Process
  • Overview of This Book
  • A Brief History of Machine Learning and Data Science Job Titles
  • Job Titles Requiring ML Experience
  • Machine Learning Lifecycle
    • Startups
    • Larger ML Teams
  • The Three Pillars of Machine Learning Roles
    • Machine Learning Algorithms and Data Intuition: Ability to Adapt
    • Programming and Software Engineering: Ability to Build
    • Execution and Communication: Ability to Get Things Done in a Team
    • Clearing Minimum Requirements in the Three ML Pillars
  • Machine Learning Skills Matrix
  • Introduction to ML Job Interviews
  • Machine Learning Job-Interview Process
    • Applying for Jobs Through Websites or Job Boards
    • Resume Screening of Website or Job-Board Applications
    • Applying via a Referral
    • Preinterview Checklist
      • Review your notes and questions that you fumbled
      • Scheduling the interview
      • Preinterview tech prep
    • Recruiter Screening
    • Overview of Main Interview Loop
      • Technical interviews
      • Behavioral interviews
      • The on-site final round
  • Summary
• 2. Machine Learning Job Application and Resume
  • Where Are the Jobs?
  • ML Job Application Guide
    • Your Effectiveness per Application
    • Job Referrals
      • Job referral example 1: Successful intern networking and outreach
      • Job referral example 2: Warm outreach to learn more about a job posting
      • Job referral example 3: Cold message
    • Networking
  • Machine Learning Resume Guide
    • Take Inventory of Your Past Experience
    • Overview of Resume Sections
      • Experience
      • Education
      • Skills summary
      • Volunteering
      • Interests
      • Additional resume sections
    • Tailoring Your Resume to Your Desired Role(s)
      • Job posting example 1: Data scientist
      • Job posting example 2: Machine learning engineer
    • Final Resume Touch-ups
  • Applying to Jobs
    • Vetting Job Postings
    • Mapping Your Skills and Experience to the ML Skills Matrix
    • Tracking Applications
  • Additional Job Application Materials, Credentials, and FAQ
    • Do You Need a Project Portfolio?
    • Do Online Certifications Help?
    • FAQ: How Many Pages Should My Resume Be?
      • What are the expectations of your region?
      • Coming from academia? Create an industry resume instead of a CV
    • FAQ: Should I Format My Resume for ATS (Applicant Tracking Systems)?
  • Next Steps
    • Browsing Job Postings
    • Identifying the Gaps Between Your Current Skills and Target Roles
  • Summary
• 3. Technical Interview: Machine Learning Algorithms
  • Overview of the Machine Learning Algorithms Technical Interview
  • Statistical and Foundational Techniques
    • Summarizing Independent and Dependent Variables
    • Defining Models
    • Summarizing Linear Regression
    • Defining Training and Test Set Splits
    • Defining Model Underfitting and Overfitting
    • Summarizing Regularization
    • Sample Interview Questions on Foundational Techniques
      • Interview question 3-1: What is L1 versus L2 regularization?
      • Interview question 3-2: How do you deal with the challenges that come with an imbalanced dataset?
      • Interview question 3-3: Explain boosting and bagging and what they can help with.
  • Supervised, Unsupervised, and Reinforcement Learning
    • Defining Labeled Data
    • Summarizing Supervised Learning
    • Defining Unsupervised Learning
    • Summarizing Semisupervised and Self-Supervised Learning
    • Summarizing Reinforcement Learning
    • Sample Interview Questions on Supervised and Unsupervised Learning
      • Interview question 3-4: What are common algorithms in supervised learning?
      • Interview question 3-5: What are some common algorithms used in unsupervised learning? How do they work?
      • Interview question 3-6: What are the differences between supervised and unsupervised learning?
      • Interview question 3-7: What are scenarios where you would use supervised learning but not unsupervised learning, and vice versa? Please illustrate with some real-world examples.
      • Interview question 3-8: What is a common issue that you might run into while implementing supervised learning, and how would you address it?
  • Natural Language Processing Algorithms
    • Summarizing NLP Underlying Concepts
    • Summarizing Long Short-Term Memory Networks
    • Summarizing Transformer Models
    • Summarizing BERT Models
    • Summarizing GPT Models
    • Going Further
    • Sample Interview Questions on NLP
      • Interview question 3-9: How would you leverage pretrained models like BERT for specific downstream tasks such as sentiment analysis, chatbots, or named entity recognition?
      • Interview question 3-10: How do you clean/process a raw text corpus for training an NLP model? Can you name one or two techniques and the reasons behind them?
      • Interview question 3-11: What are some common challenges of NLP models, and how would you address them?
      • Interview question 3-12: What is the difference between BERT-cased and BERT-uncased? What are the advantages and disadvantages of using one over the other?
  • Recommender System Algorithms
    • Summarizing Collaborative Filtering
    • Summarizing Explicit and Implicit Ratings
    • Summarizing Content-Based Recommender Systems
    • User-Based/Item-Based Versus Content-Based Recommender Systems
    • Summarizing Matrix Factorization
    • Sample Interview Questions on Recommender Systems
      • Interview question 3-13: Whats the difference between content-based recommender systems and collaborative filtering recommender systems? When would you use one over the other?
      • Interview question 3-14: What are some common problems encountered in recommender systems, and how would you resolve them?
      • Interview question 3-15: What is the difference between explicit and implicit feedback in recommender systems? What are the trade-offs with using each type, respectively?
      • Interview question 3-16: How would you address imbalanced data in recommender systems?
  • Reinforcement Learning Algorithms
    • Summarizing Reinforcement Learning Agents
    • Summarizing Q-Learning
    • Summarizing Model-Based Versus Model-Free Reinforcement Learning
    • Summarizing Value-Based Versus Policy-Based Reinforcement Learning
    • Summarizing On-Policy Versus Off-Policy Reinforcement Learning
    • Sample Interview Questions on Reinforcement Learning
      • Interview question 3-17: Explain the DQN (deep Q-network) algorithm in reinforcement learning.
      • Interview question 3-18: As a follow-up question, could you explain the main modifications that DQN added on top of regular Q-learning?
      • Interview question 3-19: Explain exploration and exploitation in reinforcement learning with an example. What are the trade-offs of these two concepts? What are some ways you would balance exploration and exploitation?
      • Interview question 3-20: In the following scenario, youve found that the reinforcement learning algorithm keeps recommending an item that is incorrectly labeled as 10% of its sale price. What might have caused this, and what would you investigate, assuming that the data is all correct?
      • Interview question 3-21: Explain model-based or model-free reinforcement learning. What are some examples of each, and when would you choose one over the other?
  • Computer Vision Algorithms
    • Summarizing Common Image Datasets
    • Summarizing Convolutional Neural Networks (CNNs)
    • Summarizing Transfer Learning
    • Summarizing Generative Adversarial Networks
    • Summarizing Additional Computer Vision Use Cases
      • Super resolution summary
      • Object detection summary
      • Semantic image segmentation summary
    • Sample Interview Questions on Image Recognition
      • Interview question 3-22: What are some common techniques of preprocessing in image-recognition tasks?
      • Interview question 3-23: How might you handle class imbalance in image-recognition tasks?
      • Interview question 3-24: How would you handle overfitting in image-recognition tasks?
      • Interview question 3-25: How would you improve and optimize the architecture for a CNN used for image recognition?
  • Summary
• 4. Technical Interview: Model Training and Evaluation
  • Defining a Machine Learning Problem
  • Data Preprocessing and Feature Engineering
    • Introduction to Data Acquisition
    • Introduction to Exploratory Data Analysis
    • Introduction to Feature Engineering
      • Handling missing data with imputation
      • Handling duplicate data
      • Standardizing data
      • Data preprocessing
        • One-hot encoding of categorical data
        • Label encoding
        • Binning for numerical values
        • Feature selection
    • Sample Interview Questions on Data Preprocessing and Feature Engineering
      • Interview question 4-1: Whats the difference between feature engineering and feature selection?
      • Interview question 4-2: How do you prevent data leakage issues while conducting data preprocessing?
      • Interview question 4-3: How do you handle a skewed data distribution during feature engineering, assuming that the minority data class is required for the machine learning problem?
  • The Model Training Process
    • The Iteration Process in Model Training
    • Defining the ML Task
    • Overview of Model Selection
    • Overview of Model Training
      • Hyperparameter tuning
      • ML loss functions
      • ML optimizers
      • Experiment tracking
      • Additional resource for model training
    • Sample Interview Questions on Model Selection and Training
      • Interview question 4-4: In what scenario would you use a reinforcement learning algorithm rather than, say, a tree-based method?
      • Interview question 4-5: What are some common mistakes made during model training, and how would you avoid them?
      • Interview question 4-6: In what scenario might ensemble models be useful?
  • Model Evaluation
    • Summary of Common ML Evaluation Metrics
      • Classification metrics
      • Regression metrics
      • Clustering metrics
      • Ranking metrics
    • Trade-offs in Evaluation Metrics
    • Additional Methods for Offline Evaluation
    • Model Versioning
    • Sample Interview Questions on Model Evaluation
      • Interview question 4-7: What is the ROC metric, and when is it useful?
      • Interview question 4-8: What is the difference between precision and recall; when would you use one over the other in a classification task?
      • Interview question 4-9: What is the NDCG (normalized discounted cumulative gain), explained on a high level? What type of ML task is it used for?
  • Summary
• 5. Technical Interview: Coding
  • Starting from Scratch: Learning Roadmap If You Dont Know Python
    • Pick Up a Book or Course Thats Easy to Understand
    • Start with Easy Questions on LeetCode, HackerRank, or Your Platform of Choice
    • Set a Measurable Target and Practice, Practice, Practice
    • Try Out ML-Related Python Packages
  • Coding Interview Success Tips
    • Think Out Loud
    • Control the Flow
    • Your Interviewer Can Help You Out
    • Optimize Your Environment
    • Interviews Require Energy!
  • Python Coding Interview: Data- and ML-Related Questions
    • Sample Data- and ML-Related Interview and Questions
      • Scenario
      • Question 5-1 (a)
      • Question 5-1 (b)
    • FAQs for Data- and ML-Focused Interviews
    • Resources for Data and ML Interview Questions
  • Python Coding Interview: Brainteaser Questions
    • Patterns for Brainteaser Programming Questions
      • Array and string manipulation
      • Sliding window
      • Question 5-2
      • Two pointers
      • Question 5-3
    • Resources for Brainteaser Programming Questions
      • Practice platforms for coding interviews
      • Curated study resources for coding interviews
      • Curated practice problems for coding interviews
  • SQL Coding Interview: Data-Related Questions
    • Resources for SQL Coding Interview Questions
  • Roadmaps for Preparing for Coding Interviews
    • Coding Interview Roadmap Example: Four Weeks, University Student
    • Coding Interview Roadmap Example: Six Months, Career Transition
    • Coding Interview Roadmap: Create Your Own!
  • Summary
• 6. Technical Interview: Model Deployment and End-to-End ML
  • Model Deployment
    • The Main Experience Gap for New Entrants into the ML Industry
    • Should Data Scientists and MLEs Know This?
    • End-to-End Machine Learning
    • Cloud Environments and Local Environments
      • Summary of local environments
      • Summary of cloud environments
        • Public cloud provider
        • On-premises and private cloud
    • Overview of Model Deployment
      • Introduction to Docker
      • Orchestrating with Kubernetes
    • Additional Tooling to Know
    • On-Device Machine Learning
    • Interviews for Roles Focused on Model Training
  • Model Monitoring
    • Monitoring Setups
      • Dashboards
      • Data quality checks
      • Alerts
    • ML-Related Monitoring Metrics
  • Overview of Cloud Providers
    • GCP
    • AWS
    • Microsoft Azure
  • Developer Best Practices for Interviews
    • Version Control
    • Dependency Management
    • Code Review
    • Tests
  • Additional Technical Interview Components
    • Machine Learning Systems Design Interview
    • Technical Deep-Dive Interview
    • Take-Home Exercise Tips
    • Product Sense
    • Sample Interview Questions on MLOps
      • Interview question 6-1: Can you walk through an example where you improved the scalability of ML infrastructure?
      • Interview question 6-2: How do you handle the monitoring and performance tracking of ML models in production?
      • Interview question 6-3: What kind of CI/CD pipeline for ML models have you built, and how?
  • Summary
• 7. Behavioral Interviews
  • Behavioral Interview Questions and Responses
    • Use the STAR Method to Answer Behavioral Questions
    • Enhance Your Answers with the Heros Journey Method
    • Best Practices and Feedback from an Interviewers Perspective
  • Common Behavioral Questions and Recommendations
    • Questions About Communication Skills
    • Questions About Collaboration and Teamwork
    • Questions on How You Respond to Feedback
    • Questions on Dealing with Challenges and Learning New Skills
    • Questions About the Company
    • Questions About Work Projects
    • Free-Form Questions
  • Behavioral Interview Best Practices
    • How to Answer Behavioral Questions If You Dont Have Relevant Work Experience
      • If youre a student
      • If you worked in another field
      • Get creativecreate your own experience
    • Senior+ Behavioral Interview Tips
  • Specific Preparation Examples for Big Tech
    • Amazon
    • Meta/Facebook
    • Alphabet/Google
    • Netflix
  • Summary
• 8. Tying It All Together: Your Interview Roadmap
  • Interview Preparation Checklist
  • Interview Roadmap Template
  • Efficient Interview Preparation
    • Become a Better Learner
      • Get hands-on ASAP
      • Understand the system
      • Progress per time spent equals efficiency
      • Iteratively fill in knowledge gaps
    • Time Management and Accountability
      • Focus time
      • Use the Pomodoro Technique
      • Do you need an accountability buddy?
    • Avoid Burnout: It Is Costly
  • Impostor Syndrome
  • Summary
• 9. Post-Interview and Follow-up
  • Post-Interview Steps
    • Take Notes of What You Remember from the Interview
    • Make Sure Youre Not Missing Important Information
    • Should You Send a Thank-You Email to the Interviewer?
    • Thank-You Note Template
    • How Long Should You Wait After the Interview for a Response Before Following Up?
  • What to Do Between Interviews
    • How to Respond to Rejections
    • Template for Rejection Responses
    • Job Applications Are a Funnel
    • Update and Customize Your Resume and Test Variations
  • Steps of the Offer Stage
    • Let Other Interviews-in-Progress Know Youve Gotten an Offer
    • What to Do If the Offer Response Timeline Is Very Short
    • Understand Your Offer
      • Workplace culture
      • Work-life balance
      • Base pay
      • Bonuses, stocks, and other kinds of compensation
      • Benefits
      • Tying it all together
  • First 30/60/90 Days of Your New ML Job
    • Gain Domain Knowledge
    • Gain Code Knowledge
    • Meet Relevant People
    • Help Improve the Onboarding Documentation
    • Keep Track of Your Achievements
  • Summary
• Epilogue
• Index