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Opis ebooka: Machine Learning Interviews
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
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Hone your machine learning skills to unlock robust models with less data through active machine learning. Tame messy datasets, conquer concept drift, and drive ML productivity with Python's active learning toolkit.
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Szczegóły ebooka
- ISBN Ebooka:
- 978-10-981-4650-4, 9781098146504
- Data wydania ebooka:
- 2023-11-29 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 10.8MB
- Rozmiar pliku Mobi:
- 20.5MB
Spis treści ebooka
- 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
- Summary of Common ML Evaluation 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
- Starting from Scratch: Learning Roadmap If You Dont Know Python
- 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)
- Sample Data- and ML-Related Interview and Questions
- 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
- Patterns for Brainteaser Programming Questions
- 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
- Model Deployment
- Additional Tooling to Know
- On-Device Machine Learning
- Interviews for Roles Focused on Model Training
- Model Monitoring
- Monitoring Setups
- Dashboards
- Data quality checks
- Alerts
- Monitoring Setups
- 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
- Behavioral Interview Questions and Responses
- 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
- How to Answer Behavioral Questions If You Dont Have Relevant Work 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
- Become a Better Learner
- 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?
- Post-Interview Steps
- 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
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- usługa obejmuje tylko książki oznaczone tagiem "Końcówka nakładu";
- wady o których mowa powyżej nie podlegają reklamacji;
Masz pytanie o konkretny tytuł? Napisz do nas: sklep[at]helion.pl.
Książka drukowana
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