Hi everyone! 
Just finished the Machine Learning Specialization and wanted to share something I built to tackle a challenge I kept facing:
The Problem: After learning about Linear Regression, Logistic Regression, Decision Trees, Neural Networks, etc., I still found myself guessing which algorithm to use for new projects.
The Solution: I created a systematic decision assistant that walks through:
- Problem type and data characteristics
- When to use each algorithm we learned about
- Mathematical concepts behind each choice (with proper LaTeX rendering!)
- Debugging strategies for bias/variance issues
- Practical implementation tips
Try it here: https://ml-decision-assistant.vercel.app/
The tool essentially codifies the systematic thinking approach taught throughout the specialization - instead of random trial-and-error, it guides you through a structured decision process.
What I’d love feedback on:
- Does this align with the systematic ML approach from the course?
- Any algorithms or considerations I should add?
- Would this be helpful for other learners going through the specialization?
Thanks for building such an amazing learning community here! The course fundamentally changed how I approach ML problems, and I hope this tool can help others apply that same systematic thinking.
