Chapter 6: Introduction to Machine Learning¶

Your first steps into machine learning—from concepts to your first model and capstone project.

Metadata¶

Learning Objectives¶

• Understand the ML pipeline (problem → solution)
• Distinguish supervised, unsupervised, and reinforcement learning
• Build your first ML model with scikit-learn
• Evaluate model performance correctly
• Understand bias-variance tradeoff
• Apply feature engineering and cross-validation
• Complete a churn prediction capstone

What's Included¶

Notebooks¶

Scripts¶

• ml_toolkit.py — Data loading, splitting, evaluation helpers
• utilities.py — Reusable ML utilities

Exercises¶

• 5 exercises with solutions (in solutions/ branch)

SVG Diagrams¶

• 3 visual diagrams for ML concepts and pipelines

Read Online¶

You can read the full chapter content right here on the website:

• 06.1 Introduction -- What is ML, types of learning, first linear regression model
• 06.2 Intermediate -- Feature engineering, evaluation metrics, bias-variance tradeoff
• 06.3 Advanced -- Customer churn prediction end-to-end capstone

Or try the code in the Playground.

How to Use This Chapter¶

1. Clone and install dependencies

git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters
pip install -r requirements.txt

2. Navigate to the chapter

cd chapters/chapter-06-intro-machine-learning

3. Launch Jupyter

jupyter notebook notebooks/01_introduction.ipynb

Created by Luigi Pascal Rondanini | Generated by Berta AI