machine-learning-interview README

The project's README from github.com/khangich/machine-learning-interview. As one markdown file: README.md.

Minimum Viable Study Plan for Machine Learning Interviews

Machine Learning System Design - Early Preview - Buy on Amazon
Machine Learning System Design Interview

Machine Learning interviews book on Amazon.
Machine Learning Interviews book on Amazon

Follow News about AI projects

Machine Learning Design

Section
1. Youtube Recommendation Youtube Recommendation Design
2. The main components in MLSD The main components in MLSD
3. LinkedIn Feed Ranking LinkedIn Feed Ranking
4. Ad Click Prediction Ad Click Prediction
5. Estimate Delivery time Estimate Delivery time
6. Airbnb Search ranking Airbnb Search ranking

Getting Started

How to Resources
List of promising companies WealthFront 2021 list.
Prepare for interview Common questions about Machine Learning Interview process.
Study guide Study guide contained minimum set of focus area to aces your interview.
Design ML system ML system design includes actual ML system design usecases.
ML usecases ML usecases from top companies
Test your ML knowledge Machine Learning quiz are designed based on actual interview questions from dozen of big companies.
One week before onsite interview Read one week check list
How to get offer? Read success stories
FAANG companies actual MLE interviews Read interview stories
Practice coding Leetcode questions by categories for MLE
Advance topics Read advance topics

Study guide

LeetCode (not all companies ask Leetcode questions)

Leetcode questions by categories

SQL

Programming

Statistics and probability

Big data (NOT required for Google, Facebook interview)

ML fundamentals

AB testing

DL fundamentals

ML system design

ML classic paper

ML productions

Food delivery

ML design common usecases

Fraud detection (TBD)

Adtech

Recommendations:

Testimonials

Intro

Acknowledgements and contributing

  1. Thanks for early feedbacks and contributions from Vivian, aragorn87 and others. You can create an Issue or Pull Request on this repo. You can also help upvote on ProductHunt

  2. If you find this helpful, you can Sponsor this project. It's cool if you don't.

  3. Thanks to this community, we have donated about $200 to HopeForPaws. If you want to support, you can contribute too on their website.

machine-learning-interview (khangich/machine-learning-interview)

Searching Database…

这是什么?这是一份精选的学习指南和资源合集,面向 FAANG 级别公司的机器学习面试。作者曾拿到 Google、LinkedIn、Snap、Coupang 和 StitchFix 的录用通知。
这是课程、书,还是只是一份链接列表?这是一个 GitHub 仓库,以学习计划的形式组织,包含主题表格,并链接到外部文章、论文、课程,以及一本单独付费的书籍或课程。它本身不是独立应用,也不是教程。
内容是怎么组织的?我应该从哪里开始?从 'Getting Started' 表格开始,它链接到学习指南、ML 系统设计案例、一份测验,以及面试前一周的检查清单。主题分为 LeetCode、SQL、编程、统计、ML/DL 基础和 ML 系统设计几个部分。
我需要有编程基础,还是只学机器学习理论就够了?两者都需要。该计划要求通过 LeetCode 练习编码(不过文中也指出,并非所有公司都要求),还要掌握 SQL 和编程语言基础(Java、Python 的 GIL 和并发),同时学习 ML/DL 理论和统计。
这里涵盖的每一场机器学习面试都必须练 LeetCode 吗?不必。作者明确指出,许多公司会跳过 LeetCode 式的编码轮,因此可以根据你的目标公司降低这部分的优先级。
这份指南适合哪些人?适合准备大型科技公司面试的机器学习工程师和数据科学家,从初级到有经验的候选人都适用。
有涵盖 ML 系统设计的案例研究吗?有。它链接到了一些真实的系统设计示例,比如 YouTube/TikTok 推荐、Airbnb 搜索排序、Uber Eats 配送优化、LinkedIn 信息流排序和广告点击预测。
这个内容有付费部分吗?有。作者另外销售一门 'Machine Learning System Design' 课程(可在 educative.io 和 interviewquery.com 上购买),以及一本亚马逊上的机器学习面试书籍,而仓库本身是免费的。
机器学习基础方面涵盖哪些主题?逻辑回归、决策树和随机森林、提升方法、K-means、PCA、SMOTE,以及判别式与生成式模型。其中几项还配有用 numpy 从零实现的练习。
有没有真正靠它拿到 offer 的人的真实评价?有。README 中收录了来自 Amazon、Facebook、NVIDIA、Microsoft 和 Intel 候选人的评价,他们都认为仓库中的测验和摘要帮助了自己的面试准备。
Creator
khangich
Size
5.3 MB
Stars
12.8K
Updated
CommitAug 31
Age
6y
Commits · 90 Days