Welcome! This guide will help you navigate and get the most out of this learning hub, regardless of your background or experience level.
If you've never written a line of code:
- Start with Python Basics — 2-3 weeks
- Learn SQL Fundamentals — 1-2 weeks
- Explore Math & Statistics Overview — 3-4 weeks in parallel
- Then pick your role track
If you know Python basics but are new to data/ML:
- Do a quick Python for Data Science refresher
- Jump into the Beginner section of your chosen track
- Don't skip the math foundations — they'll become important
If you're a developer moving into ML/AI:
- You likely know Python and Git — skip to track-specific content
- Focus on Math & Statistics if it's been a while
- Start at Intermediate level in your chosen track
If you have DS experience and want to expand:
- Check the Role Comparison to identify gaps
- Jump directly to Intermediate or Advanced sections
- Use the Skills Checklists to identify blind spots
Read the Role Comparison Guide then choose:
┌─────────────────────────────────────────────────┐
│ Do you love analyzing data and telling stories? │
│ → Data Scientist Track │
│ │
│ Do you love building reliable software systems? │
│ → ML Engineer Track │
│ │
│ Do you love building AI-powered products fast? │
│ → AI Engineer Track │
└─────────────────────────────────────────────────┘
You don't have to commit forever — many people start in one track and migrate. The fundamentals transfer.
Each track has a structured path:
Beginner → Intermediate → Advanced → Projects
Within each level, the pattern is:
1. Read the overview (README.md in each section)
2. Work through the learning materials in order
3. Complete the exercises/notebooks
4. Build the suggested mini-project
5. Check off items on the Skills Checklist
6. When 80%+ of the checklist is done → move to next level
- Build projects as you go — passive reading doesn't stick
- Use Jupyter notebooks — run every code example yourself
- Take notes — summarize concepts in your own words
- Time-box topics — spend 2-3 days max on a concept before moving on
- Join the community — ask questions in forums, Discord, GitHub Discussions
- Track your progress — check off items, celebrate milestones
- Don't try to learn everything before starting projects
- Don't follow 5 tutorials simultaneously — pick one path and finish it
- Don't skip the fundamentals — they'll haunt you later
- Don't compare your progress to others — everyone learns differently
- Don't wait to be "ready" to apply — start applying at 70% of target skills
Every hands-on section has Jupyter notebooks. Three ways to run them:
Click the "Open in Colab" button at the top of any notebook.
Click the green "Code" button on GitHub → "Codespaces" → "Create codespace".
# Clone the repo
git clone https://github.com/viraj97-sl/ai-ml-ds-learning-hub.git
cd ai-ml-ds-learning-hub
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter
jupyter lab- Fork this repo to your own GitHub account
- In each section's README, there are task checklists using
- [ ]items - Check them off as you complete them
- Your forked repo becomes your public learning portfolio!
Create a file my_progress.md in your fork and log:
- Date
- What you learned
- What you built
- What was confusing (and how you resolved it)
This becomes an incredibly powerful artifact during job interviews.
📁 Root
├── 📄 README.md — Start here, main navigation
├── 📁 00_Overview — Role comparison, salary, and this guide
├── 📁 01_Data_Scientist — Full DS learning track
│ ├── 📁 beginner — Foundations for DS
│ ├── 📁 intermediate — Core ML skills
│ ├── 📁 advanced — Expert-level topics
│ └── 📁 projects — Portfolio projects
├── 📁 02_ML_Engineer — Full MLE learning track
│ └── (same structure as DS)
├── 📁 03_AI_Engineer — Full AIE learning track
│ └── (same structure as DS)
├── 📁 04_Foundations — Math, statistics, Python — shared across tracks
├── 📁 05_Hands_On_Labs — Standalone notebooks and mini-projects
├── 📁 06_Interview_Prep — Role-specific interview questions & answers
├── 📁 07_Resources — Books, courses, papers, tools
└── 📁 08_Career_Guide — Salary, portfolio, resume, networking
Mon: Theory + Reading (2 hrs)
Tue: Notebook exercises (2 hrs)
Wed: Rest or light review
Thu: Continue notebook / Start project (2 hrs)
Fri: Project work (2 hrs)
Sat: New concept or tutorial (3 hrs)
Sun: Review, notes, community engagement (1 hr)
Morning: New concept study (3-4 hrs)
Afternoon: Hands-on coding / project work (4-5 hrs)
Evening: Review, articles, community (1-2 hrs)
Weekend: Longer projects, competitions, catch-up
"You learn to code by writing bad code first. Don't wait for perfect — ship it." — Common wisdom in the developer community
"The best project is the one you're actually interested in. Kaggle is fine, but a project you care about is better."
"Read 1 research paper per week, even if you don't understand all of it. Understanding accumulates over months."
"Get a job before you feel ready. The last 30% of skills are best learned on the job."
- Stuck on a concept? Open a GitHub Discussion in this repo
- Found a bug? Open a GitHub Issue
- Want to contribute? Read CONTRIBUTING.md
- General learning questions? See Communities for where to ask
Ready to start? → Pick your track