Machine learning interviews : kickstart your machine learning career and data career / Susan Shu Chang.

Format:

Sound recording

Author/Creator:

Chang, Susan Shu, author.

Contributor:

Brooks, Xifeng, narrator.

Language:

English

Subjects (All):

Machine learning.

Computer programming--Vocational guidance.

Computer programming.

Employment interviewing.

Physical Description:

1 online resource (1 sound file (9 hr., 39 min.))

Edition:

[First edition].

Place of Publication:

Rego Park : Ascent Audio, 2024.

[Place of publication not identified] : Ascent Audio, 2024.

System Details:

audio file

Summary:

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.

Participant:

Narrator: Xifeng Brooks.

Notes:

Unabridged.

OCLC-licensed vendor bibliographic record.

ISBN:

9781663732477

1663732477

OCLC:

1424865466