Several videos presenting the practice required to pass machine learning design interviews. It will not be boring.
Machine Learning Design Questions
3h 3m 57s
English
Paid
About the Author: AlgoExpert
AlgoExpert is a US technical-interview-preparation platform founded by Clément Mihailescu (a former Google engineer) — one of the most-used resources for the algorithm / data-structure portion of senior engineering interviews. The platform's distinctive contribution is the curated set of 160 hand-picked interview questions across difficulty tiers, each with video walkthroughs in multiple languages.
The platform has expanded beyond the original AlgoExpert track into SystemsExpert (system design), FrontendExpert (the browser / front-end interview rounds), MLExpert (machine learning interviews), ProgrammingExpert (the broader CS / coding skill foundation), and InfraExpert (infrastructure / DevOps interviews). The teaching style is rigorous and pattern-focused.
The CourseFlix listing under this source carries 8 AlgoExpert courses spanning that range. Material is paid; AlgoExpert runs on per-course or membership pricing on the original platform. Courses are aimed at engineers preparing for technical interviews at large tech companies.
Watch Online 5 lessons
0:00
/ #1: Design A Machine Learning Platform
All Course Lessons (5)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Design A Machine Learning Platform Demo | 33:59 | |
| 2 | Design Facebook Photo Tagging | 28:26 | |
| 3 | Design Amazon Alexa | 42:00 | |
| 4 | Design A Fake News Detector | 35:27 | |
| 5 | Design YouTube's Recommendation System | 44:05 |
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Frequently asked questions
What prerequisites should I have before taking this course?
Prospective students should have a foundational understanding of machine learning concepts and some experience with implementing algorithms. Familiarity with platforms and tools used in machine learning environments, along with basic programming skills, will be beneficial for tackling the design problems presented in the course.
What is the main focus of the projects in this course?
The course focuses on designing machine learning systems for real-world applications. Projects include designing a machine learning platform, Facebook photo tagging, Amazon Alexa's functionalities, a fake news detector, and YouTube's recommendation system, each emphasizing different aspects of system design and application.
Who is the target audience for this course?
This course is aimed at individuals preparing for machine learning design interviews, particularly those seeking roles that involve system design and architecture in machine learning. It's suitable for learners who want to deepen their understanding of how to construct and evaluate complex machine learning systems.
How does the depth of this course compare to other machine learning courses?
This course specifically focuses on design problems rather than algorithmic implementation or theory alone. It provides a practical approach to solving real-world machine learning design challenges, making it distinct from courses that focus purely on algorithms or theoretical aspects.
What specific tools or platforms are covered in the lessons?
The lessons do not focus on specific tools or platforms but rather concentrate on the system design aspects of machine learning applications, such as the architecture behind Facebook's photo tagging and YouTube's recommendation system.
What topics are not covered in this course?
The course does not cover the implementation of machine learning algorithms or the theoretical foundations in detail. It is specifically tailored to address design questions that might appear in interviews rather than providing a comprehensive guide to machine learning techniques.
How can this course be beneficial for career advancement?
Completing this course can enhance a candidate's ability to tackle machine learning design interview questions effectively, thereby increasing their prospects in roles that require expertise in designing complex systems. The skills gained are applicable to various domains, including tech giants like Facebook, Amazon, and Google.