Repository
Jun-ds-ml-bootcamp-2026
Data Science and Machine Learning Bootcamp. (Jun - 2026)
- #
- Title
- 1
- Lesson One — Data Science
- 2
- Lesson Two — Introduction to Machine Learning
- 3
- Assignment One
Specs
- #Title
- 1Lesson One — Data Science
- 2Lesson Two — Introduction to Machine Learning
- 3Assignment One
- 4Lesson Three — Data Foundations
- 5September Revision — Data Foundations
- 6Assignment Two
- 7Lesson Four — Data Preprocessing Pipeline
- 8Assignment Three
- 9Lesson Five (Theory) — Regression
- 10Lesson Five (Practice) — Regression in Action
- 11Regression in Jupyter Notebook
- 12Assignment Four
- 13Lesson Six (Theory) — Classification
- 14Lesson Six (Practice) — Classification Data Processing
- 15Lesson Six (Practice) — Classification in Action
- 16Assignment Five
- 17Lesson Seven (Theory) — Clustering
- 18Lesson Seven (Practice) — Clustering in Action
- 19Assignment Six
- 20Lesson Eight — Deployment
- 21Final Project
- 22Bonus — Math for Data Science & Machine Learning
- 23Bonus — Introduction to Deep Learning
- 24Bonus — AI & Generative AI
- 25Bonus — AI Ethics & Responsible AI
- 26Bonus — Career Path in Machine Learning
- 27Bonus — Building a Strong Portfolio
- 28Bonus — Final Instructor Advice
README
📊 DS-ML Bootcamp
Welcome to the Data Science & Machine Learning Bootcamp repository! 🚀
This repo contains all the lessons, code examples, and resources used during our one-month intensive bootcamp.
The program is designed to take participants from zero to hands-on projects, covering the full ML workflow:
- Collect Data
- Preprocess Data
- Split into Train/Test
- Choose Model
- Train Model
- Evaluate Model
- Deploy Model
🎯 Goal
Our mission is simple:
Move from unreal to real, from unimaginable to imaginable — by practicing the entire DS/ML journey, step by step, in just one month.
🎥 Course Videos
All lessons, assignments, and bonus sessions are available as unlisted videos on the Goobo Labs YouTube Channel.
Follow them in order for the best learning experience. 🚀
| # | Title | Watch |
|---|---|---|
| 1 | Lesson One — Data Science | ▶️ Watch |
| 2 | Lesson Two — Introduction to Machine Learning | ▶️ Watch |
| 3 | Assignment One | ▶️ Watch |
| 4 | Lesson Three — Data Foundations | ▶️ Watch |
| 5 | September Revision — Data Foundations | ▶️ Watch |
| 6 | Assignment Two | ▶️ Watch |
| 7 | Lesson Four — Data Preprocessing Pipeline | ▶️ Watch |
| 8 | Assignment Three | ▶️ Watch |
| 9 | Lesson Five (Theory) — Regression | ▶️ Watch |
| 10 | Lesson Five (Practice) — Regression in Action | ▶️ Watch |
| 11 | Regression in Jupyter Notebook | ▶️ Watch |
| 12 | Assignment Four | ▶️ Watch |
| 13 | Lesson Six (Theory) — Classification | ▶️ Watch |
| 14 | Lesson Six (Practice) — Classification Data Processing | ▶️ Watch |
| 15 | Lesson Six (Practice) — Classification in Action | ▶️ Watch |
| 16 | Assignment Five | ▶️ Watch |
| 17 | Lesson Seven (Theory) — Clustering | ▶️ Watch |
| 18 | Lesson Seven (Practice) — Clustering in Action | ▶️ Watch |
| 19 | Assignment Six | 📝 Assignment |
| 20 | Lesson Eight — Deployment | ▶️ Watch |
| 21 | Final Project | ▶️ Watch |
| 22 | Bonus — Math for Data Science & Machine Learning | ▶️ Watch |
| 23 | Bonus — Introduction to Deep Learning | ▶️ Watch |
| 24 | Bonus — AI & Generative AI | ▶️ Watch |
| 25 | Bonus — AI Ethics & Responsible AI | ▶️ Watch |
| 26 | Bonus — Career Path in Machine Learning | ▶️ Watch |
| 27 | Bonus — Building a Strong Portfolio | ▶️ Watch |
| 28 | Bonus — Final Instructor Advice | ▶️ Watch |
📘 Tip:
You can clone the repo, open Jupyter/VS Code, and follow along while watching each lesson for hands-on learning.
🙌 Contributors
This bootcamp is proudly hosted by:
Together, our goal is to empower the next generation of data scientists.
🤝 Sponsored By
This bootcamp is fully sponsored by:
📄 License
Copyright © 2026 Goobo Labs. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
You are free to share and adapt this material for non-commercial purposes, as long as you give appropriate credit and distribute your contributions under the same license. See LICENSE for the full legal text.
⭐ If you find this useful, don’t forget to star the repo!