Inside The Machine Learning Interview
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Inside the Machine Learning Interview
Let Peng Shao, a former engineering leader at Amazon and Staff ML Engineer at Twitter, be your guide to mastering Machine Learning interviews. The book includes: a comprehensive breakdown of the ML interview process, including all the major interview session--: ML Fundamentals, ML Coding, ML System Design, & ML Infrastructure; proven strategies for approaching and solving a wide range of ML problems, drawing from real-world scenarios; step-by-step guidance on tackling ML coding challenges, system design questions, and infrastructure design problems; a deep dive into the mindset of interviewers, understanding what they value and how to effectively demonstrate your expertise; practical examples and case studies showcasing the history of solutions to ML problems, from pioneering approaches to the state of the art.
Machine Learning Interviews
Author: Susan Shu Chang
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-11-29
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
Machine Learning Interview Guide
DESCRIPTION This book prepares you with the knowledge and skills to confidently excel in the exciting world of machine learning (ML) interviews and launch a successful career in this dynamic field. This book offers a collection of curated questions and answers to help readers understand key ML concepts, including data processing, classification, regression, clustering, dimensionality reduction, time series, and natural language processing (NLP). While not exhaustive, it focuses on critical topics and common questions often encountered in interviews. The chapters highlight essential concepts without a strict order of importance, reflecting the informal nature of ML interviews. Alongside theoretical knowledge, the book emphasizes the importance of coding and real-world application for a deeper understanding. Practical exercises, coding projects, and continuous learning are crucial to mastering ML concepts. By mastering the concepts and question-answer formats presented in this book, you will be well-prepared to tackle technical interview challenges and confidently showcase your ML expertise. This guide will help you achieve your career goals in the exciting field of ML. KEY FEATURES ● Major topics and concepts covered in a question-answer format. ● One can gain expertise in how to present an answer during an ML interview. ● Helps to structure the interview process and make it streamlined as per the industry. WHAT YOU WILL LEARN ● Understand core data concepts for ML. ● Master classification and regression algorithms. ● Learn clustering and dimensionality reduction techniques. ● Analyze and forecast time-dependent data with time series analysis. ● Gain NLP proficiency and understand human language with techniques like tokenization, stemming, lemmatization, and advanced language models. WHO THIS BOOK IS FOR This book can be used by an interviewee, interviewer, ML professionals who want to learn the interview structure, and ML practitioners who want to refresh their memory and use this book as a reference guide. Managerial and non-technical people can use this book to learn ML in unique ways through a question-answer format. TABLE OF CONTENTS 1. Data Processing for Machine Learning 2. Classification 3. Regression 4. Clustering and Dimensionality Reduction 5. Time Series 6. Natural Language Processing