
Machine Learning Interviews
Kickstart Your Machine Learning and Data Career
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Narrado por:
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Xifeng Brooks
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De:
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Susan Shu Chang
Acerca de esta escucha
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, and data scientist; 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; and prepare for interviews in statistics and machine learning theory by studying common interview questions.
PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
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Historia
How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the world’s leading practitioners construct and maintain software. This book covers Google’s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization.
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not useful
- De Andreas Andersen en 07-21-21
De: Titus Winters, y otros
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Fundamentals of Data Engineering
- Plan and Build Robust Data Systems
- De: Joe Reis, Matt Housley
- Narrado por: Adam Verner
- Duración: 17 h y 31 m
- Versión completa
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Historia
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
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Great for Hands on Business owners
- De LmL en 08-14-24
De: Joe Reis, y otros
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Why Machines Learn
- The Elegant Math Behind Modern AI
- De: Anil Ananthaswamy
- Narrado por: Rene Ruiz
- Duración: 13 h y 30 m
- Versión completa
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Historia
We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.
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A great listen, but a physical book is pre appropriate
- De Sameer D. en 11-07-24
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Designing Data-Intensive Applications
- The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
- De: Martin Kleppmann
- Narrado por: Benjamin Lange
- Duración: 20 h y 56 m
- Versión completa
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Historia
Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.
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Must read for senior+ software engineers
- De Navid v en 05-29-21
De: Martin Kleppmann
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Software Architecture: The Hard Parts
- Modern Trade-Off Analyses for Distributed Architectures
- De: Neal Ford, Mark Richards, Pramod Sadalage, y otros
- Narrado por: Dena Dahilig
- Duración: 12 h y 53 m
- Versión completa
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Historia
There are no easy decisions in software architecture. Instead, there are many hard parts—difficult problems or issues with no best practices—that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture.
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Great content. Terrible narration.
- De Alex en 12-16-23
De: Neal Ford, y otros
Lo que los oyentes dicen sobre Machine Learning Interviews
Calificaciones medias de los clientesReseñas - Selecciona las pestañas a continuación para cambiar el origen de las reseñas.
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Historia
- Goodmann
- 01-26-25
Great advice, and read well.
Solid and structure advice for ML interviews. Except for the code parts, a very nice audio experience.
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