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Machine Learning

Machine Learning Interviews in 3 Weeks

Published by O'Reilly Media, Inc.

Beginner to intermediate content levelBeginner to intermediate

Prepare for coding assessments, machine learning theory, behavioral questions, and more

This live event utilizes Jupyter Notebook technology
  • Learn the skill sets required for each type of machine learning role and evaluate your strengths and weaknesses
  • Understand the fundamentals of machine learning interviews, such as coding assessments, statistics and machine learning theory, behavioral questions, and more
  • Explore interview case studies and actively identify how to succeed in them

Join expert Susan Shu Chang to give yourself a competitive advantage by learning how to ace the highly selective interview recruitment process and break into the rapidly growing ML field.

Week 1: Introduction to Machine Learning Roles and Skill Sets

Week 2: What Interviews Assess and How to Succeed at Them

Week 3: Behavioral Interview Questions, Case Studies, and Setting Up an Action Plan

NOTE: With today’s registration, you’ll be signed up for all three weeks. Although you can attend any of the sessions individually, we recommend participating in all three weeks.

What you’ll learn and how you can apply it

  • Evaluate your current skills to close any gaps that may prevent you from succeeding in the interview process
  • Prepare for coding exams with tried-and-true resources
  • Prepare for interviews in statistics and machine learning theory
  • Frame your past projects and present your portfolio in the best light, even without previous work experience
  • Answer interview questions on a past project deep dive
  • Beef up your resume structure to get more interviews

This live event is for you because...

  • You’re a recent graduate who is eager to become a machine learning practitioner in industry.
  • You’re a software engineer or (product) data scientist who is transitioning into a machine learning role.
  • You need an action plan to be a competitive machine learning job candidate.

Prerequisites

  • Python programming knowledge (basic to intermediate)
  • Machine learning theory and/or statistics (basic to intermediate)

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Week 1

Introduction to machine learning roles and skill sets

  • Group discussion: What stage are you at in your machine learning job search?
  • Presentation: Overview of the machine learning field; the various roles available in the field; general machine learning interview process; identifying the gaps between role and skill set
  • Hands-on exercise: Write down your machine learning skills
  • Q&A

Week 2

What interviews assess and how to succeed at them

  • Group discussion: Since last session, what have you put into practice?
  • Presentation: Specific machine learning interview building blocks; real interview questions and what the questions aim to identify; answering questions optimally; improving your ML portfolio; organizing your past projects and demonstrating your skills during the interview
  • Hands-on exercise: Identify interview areas you are most confident about and where you might need additional preparation
  • Group discussion: Refine your past portfolio experience
  • Q&A

Week 3

Behavioral interview questions, case studies, and setting up an action plan

  • Group discussion: Your data science/machine learning project portfolio
  • Presentation: End-to-end machine learning interview case studies; project deep dive and systems design questions; structuring a compelling resume and career story; developing an action plan
  • Hands-on exercise: Make a rough schedule of preparation and establish a timeline
  • Group discussion: Improve your interview preparation plan
  • Q&A

Your Instructor

  • Susan Shu Chang

    Susan Shu Chang is a principal data scientist at Elastic (of Elasticsearch), with previous ML experience in fintech, telecommunications, and social platforms. Susan is an international speaker, with talks at six PyCons worldwide and keynotes at Data Day Texas, PyCon DE & PyData Berlin, and O’Reilly’s AI Superstream. She writes about machine learning career growth in her newsletter, susanshu.substack.com. In her free time she leads a team of game developers under Quill Game Studios, with multiple games released on consoles and Steam.

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