Welcome to my public repository where I share my journey and materials used for preparing coding interviews for a Machine Learning Engineer role. This repository is inspired by the guidelines provided in the Tech Interview Handbook and is supplemented by my own learning experiences documented on a Notion page.

The materials in this repository are organized into two main directories, each containing subdirectories and files related to specific problem-solving techniques and coding challenges:

• Problems: This directory contains subdirectories categorized by problem types.
• Techniques: This directory is structured to cover various algorithmic techniques. Within it, you'll find subdirectories for each of the data structures covered with different strategies for problems. Each problem is meant to be executed with the debug console in order to adquire a schematized view on how each technique can be applied on the different data structures.

Visit my Notion page to see my learning diary, which serves as a dynamic record of my preparation process.

• Daily learning updates
• Key insights and reflections
• Progress tracking

Didactic materials prepared by myself are available for self-study, complete with illustrative examples and explanatory texts derived from algorithms discussed in the Tech Interview Handbook. The diary includes:

• Illustrative Materials
• External Resources: Curated links to websites offering additional learning materials, including other platforms that provide access to books and problem sets.
• Exams and Solutions
• Glossary and Key Concepts
• Helper Scripts

Each data structure section within the diary is further subdivided into:

• Techniques: Detailed explanations and use cases for different algorithms and techniques, such as Sliding Window, Two Pointers, etc.
• Problems Log: A log of problems solved, categorized by technique, complete with the date last reviewed to track progress and revisions.

Contributions are welcome! If you have suggestions, improvements, or additional resources that can aid others in their ML Engineer interview preparation, feel free to open a pull request, issue or directly reach out to me at [email protected].

A big thank you to the authors of the Tech Interview Handbook for their invaluable guidance, and to everyone in the open-source community for their collective wisdom and resources that have been valuable in my journey.