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
Publisher resources
Table of contents
- Preface
-
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
- The Three Pillars of Machine Learning Roles
- Machine Learning Skills Matrix
- Introduction to ML Job Interviews
- Machine Learning Job-Interview Process
- Summary
- 2. Machine Learning Job Application and Resume
-
3. Technical Interview: Machine Learning Algorithms
- Overview of the Machine Learning Algorithms Technical Interview
- Statistical and Foundational Techniques
- Supervised, Unsupervised, and Reinforcement Learning
- Natural Language Processing Algorithms
- Recommender System Algorithms
-
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
- Computer Vision Algorithms
- Summary
- 4. Technical Interview: Model Training and Evaluation
- 5. Technical Interview: Coding
-
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
- Overview of Model Deployment
- Additional Tooling to Know
- On-Device Machine Learning
- Interviews for Roles Focused on Model Training
- Model Monitoring
- Overview of Cloud Providers
- Developer Best Practices for Interviews
- Additional Technical Interview Components
- Summary
-
Model Deployment
- 7. Behavioral Interviews
- 8. Tying It All Together: Your Interview Roadmap
- 9. Post-Interview and Follow-up
- Epilogue
- Index
- About the Author
Product information
- Title: Machine Learning Interviews
- Author(s):
- Release date: December 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781098146542
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