Machine Learning Interview Preparation (MLIP)

Intro

Problem: Preparing for ML engineer interview can take a lot of time to cover range of technical areas i.e big data, ML fundamental, Deep Learning fundamentals, Leetcode etc. I have only one goal: save your time. I've been helping around 50 people to prepare for MLE interview. Now I want to do it at scale.
Solution: provide a minimum set of focus area which covers all interview questions. I have interviewed at a lot of big companies (FAAG, MS, SnapChat, StitchFix, Intuit etc). I will not shared interview questions because of NDA.

How to prepare for interview?

Before you prepare for interiview, you can read common questions.
If you have never learned about ML, you should first learn coursera machine leanring by Andrew Ng or similar online courses.
What should I study? Use the study guide as a collections of focused areas.
Am I ready for interview? Use readyness tests to see if you're ready for interview.
How do I prepare for ML system design? Use ML usecase to go practice actual ML system design usecases.
For advance topics, see advance

LeetCode

I use this LC time tracking to keep track how many times and how long I spent each time for each question. Once I finish non-trivial medium LC question 3 times, I have absolutely no issue to solve them in the actual interview sometime within 8-10 minutes. It makes a big difference.

Leetcode questions by categories

SQL

Know SQL join: self join, inner, left, right etc.
Use hackerrank to practice SQL.

Programming

Statistics and probability

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? Alice has 2 kids and one of them is a girl. What is the probability that the other child is also a girl? 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?
Assume we have a bag with 6 coins in it. 5 coins are fair, and 1 coin has heads on both sides. I randomly picked one coin from the bag, tossed it 3 times, and they were all heads. If I toss the coin again, what is the probability that it is still heads?
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

Spark architecture and Spark lessons learned (outdated since Spark 3.0 release)
Spark OOM
Cassandra best practice and here

ML fundamentals

Collinearity and read more
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 vecrtorize version in numpy and complete in 20 minutes. 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. Bonus point for vecrtorize version in numpy and complete in 20 minutes. Followup with worst case time complexity and improvement for initialization.

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
Ad click prediction trend
Rules of ML
An Opinionated Guide to ML Research. Valueable advices in Personal development section at the bottom.

ML productions

Food delivery

Fraud detection (TBD)

Adtech

Recommendations:

Provide feedbacks

You can create Issue or Pull Request on this repo.
Send ane mail to helppreparemle@gmail.com.

About

Machine Learning Interview Preparation from FAAG, Snapchat, Intuit, StitchFix etc.