Introduction to Machine Learning Interviews Book
Target audience
About the questions
About the answers
Gaming the interview process
Acknowledgments
About the author
Part I. Overview
Chapter 1. Machine learning jobs
1.1 Different machine learning roles
1.1.1 Working in research vs. working in production
1.1.2 Research
1.1.2.1 Research vs. applied research
1.1.2.2 Research scientist vs. research engineer
1.1.3 Production
1.1.3.1 Production cycle
1.1.3.2 Machine learning engineer vs. software engineer
1.1.3.3 Machine learning engineer vs. data scientist
1.1.3.4 Other technical roles in ML production
1.1.3.5 Understanding roles and titles
1.2 Types of companies
1.2.1 Applications companies vs. tooling companies
1.2.2 Enterprise vs. consumer products
1.2.3 Startups or big companies
Chapter 2. Machine learning interview process
2.1 Understanding the interviewers’ mindset
2.1.1 What companies want from candidates
2.1.1.1 Technical skills
2.1.1.2 Non-technical skills
2.1.1.3 What exactly is culture fit?
2.1.1.4 Junior vs senior roles
2.1.1.5 Do I need a Ph.D. to work in machine learning?
2.1.2 How companies source candidates
2.1.3 What signals companies look for in candidates
2.2 Interview pipeline
2.2.1 Common interview formats
2.2.2 Alternative interview formats
2.2.3 Interviews at big companies vs. at small companies
2.2.4 Interviews for internships vs. for full-time positions
2.3 Types of questions
2.3.1 Behavioral questions
2.3.1.1 Background and resume
2.3.1.2 Interests
2.3.1.3 Communication
2.3.1.4 Personality
2.3.2 Questions to ask your interviewers
2.3.3 Bad interview questions
2.4 Red flags
2.5 Timeline
2.6 Understanding your odds
Chapter 3. After an offer
3.1 Compensation package
3.1.1 Base salary
3.1.2 Equity grants
3.1.3 Bonuses
3.1.4 Compensation packages at different levels
3.2 Negotiation
3.2.1 Compensation expectations
3.3 Career progression
Chapter 4. Where to start
4.1 How long do I need for my job search?
4.2 How other people did it
4.3 Resources
4.3.1 Courses
4.3.2 Books & articles
4.3.3 Other resources
4.4 Do’s and don’ts for ML interviews
4.4.1 Do’s
4.4.2 Don’ts
Part II: Questions
Chapter 5. Math
Notation
5.1 Algebra and (little) calculus
5.1.1 Vectors
5.1.2 Matrices
5.1.3 Dimensionality reduction
5.1.4 Calculus and convex optimization
5.2 Probability and statistics
5.2.1 Probability
5.2.1.1 Basic concepts to review
5.2.1.2 Questions
5.2.2 Stats
Chapter 6. Computer Science
6.1 Algorithms
6.2 Complexity and numerical analysis
6.3 Data
6.3.1 Data structures
Chapter 7. Machine learning workflows
7.1 Basics
7.2 Sampling and creating training data
7.3 Objective functions, metrics, and evaluation
Chapter 8. Machine learning algorithms
8.1 Classical machine learning
8.1.1 Overview: Basic algorithm
8.1.2 Questions
8.2 Deep learning architectures and applications
8.2.1 Natural language processing
8.2.2 Computer vision
8.2.3 Reinforcement learning
8.2.4 Other
8.3 Training neural networks
Appendix
A. For interviewers
The zen of interviews
B. Building your network
Published with HonKit
8.1 Classical machine learning
8.1 Classical machine learning
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