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Currently this repo contains:
- Phone screen ML interview prep questions (algorithms, validation methods, stats)
- Practical coding sessions -- I recommend setting aside 90 minutes before starting any of these challenges in order to simulate the conditions a coding sessions during an ML onsite. View solutions only once you have completed your solution, and feel free to fork this repo to add your solutions!
- Code review checklist
- CS Practical Grading Rubric
- ML Jeopardy
- A Guide to GitHub & Collaborating
- Version Control with Git/Github
- Dockerized Data Science, a presentation from Manifold.AI on using Docker for ML models.
System Design
- ML System Design Flashcards, huge thanks goes out to Chip Huyen's ML repo for assisting!
- ML Terminology Falshcards, great to use for ML charades! Shoutout to Google Developers for assisting!
- System Design Outline
- System Design Workshop
Cloud Computing Resources: AWS
- AWS in plain English
- AWS Basics
- AWS Securitythings to know when getting started.
- Setting up your deep learning AMI
- Instructions for connecting Atom IDE to EC2
- Instructions for connecting to Jupyter on an EC2 instance
- Mounting S3 Bucket to EC2 Instance
- Boto + S3 bucket example
- Intro to SageMaker
- Sagemaker Overview
- Sagemaker Training Example
- Sagemaker Inference Example
Behavioral
- Questions to ask hiring managers before accepting an offer + negotiation tips
SWE for MLEs
- Data Structures and Algorithms
- Object Oriented Programming
- OOP w/ Python review
- Intro to Dynamic Programming
Intro to Data for MLEs