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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

Final Model Development – ML Capstone Execution

🚀 Final Model Development

Consolidate your best ideas into a production-ready pipeline: preprocessing, model, evaluation, and reproducibility.

Build the Pipeline

Freeze feature list + preprocessing (scaling, encoding, imputation)
Lock random seeds; capture versions (Python, libs)
Wrap in a function/class for reuse

Data & Labels

Re-verify leakage; re-split train/val/test (stratify if classification)
Handle class imbalance (weights/SMOTE) if needed
Document any dropped rows/features

Deliverables (Lesson 1)

Pipeline script/notebook with deterministic outputs
Model card (draft): data, metrics, assumptions, limits
Saved artifacts: encoder, scaler, model (joblib/pkl)

Self-check: Can a teammate run your pipeline and reproduce the same metrics on the same split?

Capstone Project Preparation – Quiz

Model Evaluation and Tuning – ML Capstone

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