Minimum Viable Study Plan for Machine Learning Interviews
Machine Learning System Design - Early Preview - Buy on Amazon
Machine Learning interviews book on Amazon.
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Most popular post: One lesson I learned after solving 500 leetcode questions
Oct 10th: Machine Learning System Design course became the number 1 ML course on educative.
June 8th: launch interview stories series.
April 29th: I launched mlengineer.io blog so you can get latest machine learning interview experience.
April 15th 2021: Machine Learning System Design is launched on interviewquery.com.
Feb 9th 2021: Machine Learning System design is now available on educative.io.
I'm a SWE, ML with 10 years of experience (Linkedin profile). I had offers from Google, LinkedIn, Coupang, Snap and StichFix. Read my blog.
Machine Learning Design
| Section | | | ------------- | ------------- | | 1. Youtube Recommendation | 
| | 2. The main components in MLSD | 
| | 3. LinkedIn Feed Ranking | 
| | 4. Ad Click Prediction| 
| | 5. Estimate Delivery time| 
| | 6. 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)
NOTE: there are a lot of companies that do NOT ask leetcode questions. There are many paths to become an MLE, you can create your own path if you feel like leetcoding is a waste of time.
I use LC time tracking to keep track of how many times I solves a question and how long I spent each time. Once I finish non-trivial medium LC questions 3 times, I have absolutely no issues solving them in actual interviews (sometimes within 8-10 minutes). It makes a big difference. A better way is to use LeetPlug chrome extension here
Leetcode questions by categories
SQL
Know SQL join: self join, inner, left, right etc.
Use hackerrank to practice SQL.
Revise/Learn SQL Window Functions: window functions
Programming
Java garbage collection
Python pass-by-object-reference
Python GIL, Fluent Python, chapter 17
Python multithread
Python concurrency, Fluent Python, chapter 18
Statistics and probability
The only cheatsheet that you''ll ever need
Learn Bayesian and practice problems in Bayesian
Let A and B be events on the same sample space, with P (A) = 0.6 and P (B) = 0.7. Can these two events be disjoint?
Given that Alice has 2 kids, at least one of which is a girl, what is the probability that both kids are girls? (credit swierdo)
A group of 60 students is randomly split into 3 classes of equal size. All partitions are equally likely. Jack and Jill are two students belonging to that group. What is the probability that Jack and Jill will end up in the same class?
Given an unfair coin with the probability of heads not equal to .5. What algorithm could you use to create a list of random 1s and 0s.
Big data (NOT required for Google, Facebook interview)
Spark architecture and Spark lessons learned (outdated since Spark 3.0 release)
Spark OOM
Cassandra best practice and here, link
), cassandra performance
Practice problem finding friends with MapReduce
Everything in one page.
ML fundamentals
Collinearity and read more
Features scaling
Random forest vs GBDT
SMOTE synthetic minority over-sampling technique
Compare discriminative vs generative model and extra read
Logistic regression. Try to implement logistic regression from scratch. Bonus point for vectorized version in numpy + completed in 20 minutes sample code from martinpella. Followup with MapReduce version.
Quantile regression
L1/L2 intuition
Decision tree and Random Forest fundamental
Explain boosting
Least Square as Maximum Likelihood Estimator
Maximum Likelihood Estimator introduction
Kmeans. Try to implement Kmeans from scratch sample code from flothesof.github.io. Bonus point for vectorized version in numpy + completed in 20 minutes. Follow-up with worst case time complexity and improvement for initialization.
Fundamentals about PCA
I didn't use flashcard but I'm sure it helps up to certain extend.
AB testing
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
DL fundamentals
The deep learning book. Read Part ii
Machine Learning Yearning. Read from section 5 to section 27.
Neural network and backpropagation
Activation functions
Loss and optimization
Convolution Neural network notes
Recurrent Neural Networks
ML system design
ML classic paper
Technical debt in ML
Rules of ML
An Opinionated Guide to ML Research. There is valuable advice in the Personal development section at the bottom.
ML productions
Scaling ML at Uber
DL in production
Food delivery
Uber eats trip optimization
Uber food discovery
Personalized store feed
Doordash dispatch optimization
ML design common usecases
ML system design primer
Video recommendation
Feed ranking
Fraud detection (TBD)
Adtech
Ad click prediction trend
Ad Clicks CTR
Delayed feedbacks
Entity embedding
Star space, embedding all the things
Twitter timeline ranking
Recommendations:
Instagram explore
TikTok recommendation
Deep Neural Networks for YouTube Recommendations
Wide & Deep Learning for Recommender Systems
Testimonials
V, Amazon L5 DS
I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews.
K, Facebook MLE
I really like what you've built, it'll help a lot of engineers.
D, NVIDIA DS
I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!
A, Booking
Woow this is very useful summaries, so nice.
H, Microsoft
That's incredible!
V, Intel
The repo is extremely cohesive! Thanks again.
Intro
This repo is written based on REAL interview questions from big companies and the study materials are based on legit experts i.e Andrew Ng, Yoshua Bengio etc.
I have 6 YOE in Machine Learning and have interviewed more than dozen big companies. This is the minimum viable study plan that covers all actual interview questions from Facebook, Amazon, Apple, Google, MS, SnapChat, Linkedin etc.
If you're interested to learn more about paid ML system design course, click here. This course will provide 6-7 practical usecases with proven solutions. After this course you will be able to solve new problem with systematic approach.
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.
