Who Is This Series For?
This series is for beginners learning ML from scratch, especially if you:
- Know basic Python but not confident in doing ML projects.
- Have learned a few discrete algorithms but have not yet assembled them into a complete process.
- Want to learn in an easy-to-understand way, practice a lot, avoid overloading formulas.
How Will You Learn?
Each lesson is designed according to a fixed frame:
- Intuition: understand the "why" first.
- Code: run the minimal example, edit parameters to see the difference.
- Just enough math: only learn the parts needed to read results and debug models.
- Checklist: know clearly what you must do after completing your studies.
Output After Series
Once completed, you will have:
- 1 reusable end-to-end ML pipeline.
- Skills to choose the right metrics and avoid data leakage.
- Experience using pipeline, cross-validation, standard tuning.
- 1 capstone project with clear rubrics to include in the portfolio.
Fast Track
- Part 0: Getting started for newbies (setup + first model + baseline).
- Part 1: Supervised learning foundation (regression/classification/metrics).
- Part 2: Industrial Workflow (pipeline/CV/leakage/error analysis).
- Part 3: Advanced algorithms just enough to use (tree, clustering, time series).
- Part 4: Production + Explainability + Capstone.
Suggested Ways to Study Not to Be Confused
- Study 2-3 lessons each week, prioritize completing practice exercises.
- Don't jump right into a complex model before you have a baseline.
- For each lesson, record 3 ideas: hypothesis, results, lessons learned.
If you're completely new, start in the correct order from Part 0.