10 / 2026 / Data Science / ML
Machine Learning Tracker
Self-paced machine learning study needed a clearer structure than scattered notes, with progress and submission tracking visible from the start.
Client
Context and business domain
00 / Design System
Palette Logic
Data Science / ML
10
2026
Typography
Inter
Aa Bb Cc Dd Ee Ff Gg Hh Ii Jj Kk Ll Mm Nn Oo Pp Qq Rr Ss Tt Uu Vv Ww Xx Yy Zz 0123456789
Layout Grid
12-column responsive index structure.
Deep Slate
#0D1B1E
Graphite
#3D3D3D
Clear White
#F8F8F8
Soft Signal
#C9FBC6
Connected System
Learning Productivity Tool connects the visual language, interaction rhythm, and evidence structure for this case study.
01 / Challenge
The Challenge
Self-paced machine learning study needed a clearer structure than scattered notes, with progress and submission tracking visible from the start.
02 / Methodology
Methodology / Experiment
- 01
Roadmap Framing
Turned the ML learning journey into a weekly progression from foundations toward deeper AI topics.
12-week path84% - 02
Task Status Design
Created a task management structure so study items can move through clear progress states.
Task board82% - 03
Submission Tracking
Added a portfolio-like layer for notebooks, repositories, or learning evidence to be saved against the roadmap.
Evidence log80%
03 / Solution
The Solution
ML Roadmap Landing
The homepage introduces a structured machine learning journey with a clear start action and GitHub access.
Systematic Study Interface
Feature sections frame roadmap, task management, and submission tracking as a focused study system.
04 / Impact
Impact & Metrics
12
Week Roadmap
The app positions the learning flow as a twelve-week machine learning track.
Tasks
Study States
The tracker supports task progress for structured learning follow-through.
Live
Vercel Deploy
The project is available as a public web application.