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.
Author(s): Susan Shu Chang
Publisher: O'Reilly Media, Inc.
Year: 2023
Language: English
Pages: 108
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General Ebook Solution
1. Overview of the interview process
The goal of interviews and hiring
How to navigate confusing job titles
Application and resume screening
Recruiter call
Technical interviews
Behavioral interviews
Summary
2. The application
The goal of the application
Where are the jobs at?
Types of machine learning roles
Preparation for the application
Take inventory of your past experience
Make detailed lists
Map your experience to ML skills matrix
Tailor your resume to your desired role(s)
Do you need a project portfolio?
How important are certifications?
Job referrals
Next steps
Identifying the gaps between your current skills and target roles
Example scenario 1
Example scenario 2
Effective interview preparation
Activity
Questionnaire
Terminology
3. The Interview: Technical Skills – Machine Learning algorithms
Overview of Machine learning algorithms technical interview
Statistical techniques
Summarizing Independent and dependent variables:
Defining Models:
Summarizing Linear regression
Defining Train and test set splits
Defining Model overfitting and underfitting
Summarizing Regularization
Sample interview questions on statistical techniques
Supervised vs. unsupervised vs. reinforcement learning
Defining Labeled data
Summarizing Supervised learning
Defining Unsupervised learning
Summarizing Reinforcement learning
Chapter questions
Natural Language Processing algorithms
Summarizing How NLP works
Summarizing Transformer models
Summarizing LSTM (Long Short Term Memory)
Summarizing BERT (Bidirectional Encoder Representations from Transformers)
Summarizing GPT (Generative Pre-trained Transformer) models
Sample interview questions on NLPs
Recommender systems algorithms
Summarizing Collaborative filtering
Summarizing Explicit and implicit ratings
Summarizing Content based recommender systems
Summarizing Matrix factorization
Sample interview questions on recommender systems
Reinforcement learning algorithms
Summarizing Reinforcement learning agent
Summarizing Model based vs. model free reinforcement learning
Summarizing Value based vs. policy-based reinforcement learning
Summarizing On policy vs. off policy reinforcement learning
Sample interview questions on reinforcement learning
Computer vision algorithms
Convolutional neural networks (CNN)
Sample interview questions on image recognition
4. Behavioral Interview and case study interviews
How to structure your answers to behavioral questions
Hero’s journey method
Tips for senior+ roles
Common questions and examples
General advice
Case studies
Case study examples in FAANG
Machine learning systems design questions
Technical deep dive interview questions
About the Author
