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Written Edition English Tutorial

Machine Learning with Python: From Basics to Capstone

Curriculum

12 Sections
49 Lessons
10 Weeks

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Module 1: Introduction to Machine Learning
Give students a clear foundation in what Machine Learning is, how it differs from related fields, and where it’s applied in real life.
6
Module 2: Python Essentials for ML
5
Module 3: Data Wrangling & Visualization, starting with:
5
Module 4: Data Preprocessing
5
Module 5: Regression Algorithms!
This module introduces the foundational concept of regression in machine learning — perfect for prediction tasks like house prices, salaries, etc.
5
Module 6: Classification Algorithms
5
Module 7: Decision Trees
6
Module 8: Model Evaluation & Tuning
5
Module 9: Unsupervised Learning
5
Module 10: Dimensionality Reduction
5
Module 11: Capstone Project Preparation
5
Module 12: Capstone Project Execution
5

Group Presentation and Viva – ML Capstone Execution

🎤 Group Presentation & Viva

Present your work clearly, defend your choices, and demonstrate real understanding of trade-offs and deployment.

Rubric (100 pts)

Problem clarity & business value – 20
Method & validation rigor – 25
Results & interpretation – 25
Communication & visuals – 15
Q&A (viva) depth – 15

Sample Viva Questions

How did you prevent data leakage?
Why this metric and not alternatives?
What breaks in production & how to monitor?
Which ethical risks did you consider?

Presentation Logistics

Time: 10–12 min talk + 5 min Q&A
All members must speak (share ownership)
Live demo optional but encouraged (backup screenshots!)
Submit deck + report + repo link 24h before

Self-check: Can you explain your model to a non-technical stakeholder in 60 seconds with one chart?

Final Report and Presentation – ML Capstone

Capstone Project Execution – Model Development to Final Presentation

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