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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

Introduction to PCA – Principal Component Analysis

📐 Introduction to PCA

Project data onto new orthogonal axes (principal components) that capture the maximum variance with minimal dimensions.

Core Ideas

Unsupervised linear projection
PCs are orthogonal directions of max variance
Explained variance ratio guides how many PCs

When to Use

High-dimensional tabular data
Noise reduction & visualization
Preprocessing before clustering/ML

Important Notes

Scale features (StandardScaler) first
PCA is sensitive to outliers
PCs maximize variance, not class separability

Self-check: Why is scaling critical before PCA?

Curse of Dimensionality in Machine Learning – Explained

PCA in Scikit-Learn – Pipelines, n_components & Explained Variance

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