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. logo Hello, Welcome to EPUB Reader Click button to select your book Open EPUB book This Online Web App is made by Neo Reader for experimental purpose, it is a very simple EPUB Reader. We recommend you try our Neo Reader for better experience. Take a look now neat reader pc AD Ultimate EPUB Reader Totally free to try Support multiple file types, such as EPUB, MOBI, AZW3, AZW, PDF and TXT. Learn more about Neo Reader 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
