Machine Learning Zoomcamp

This repository contains my personal notes and homework of the Machine Learning Zoomcamp.

Notes

Module	Editable Version	Readable Version
Launch Stream	Marimo Notebook	Rendered HTML
Introduction to Machine Learning	Marimo Notebook	Rendered HTML
Linear Regression	Marimo Notebook	Rendered HTML
Classification	Marimo Notebook	Rendered HTML
Evaluation	Marimo Notebook	Rendered HTML
Deployment	Marimo Notebook	Rendered HTML
Decision Trees	Marimo Notebook	Rendered HTML
Deep Learning	Marimo Notebook	Rendered HTML
Deploy with AWS Lambda	Marimo Notebook	Rendered HTML
Deploy with Kubernetes	Marimo Notebook	Rendered HTML

Homework

Module	Editable Version	Readable Version
Introduction to Machine Learning	Marimo Notebook	Rendered HTML
Linear Regression	Marimo Notebook	Rendered HTML
Classification	Marimo Notebook	Rendered HTML
Evaluation	Marimo Notebook	Rendered HTML
Deployment	Marimo Notebook	Rendered HTML
Decision Trees	Marimo Notebook	Rendered HTML
Deep Learning	Marimo Notebook	Rendered HTML
Deploy with AWS Lambda	Marimo Notebook	Rendered HTML
Deploy with Kubernetes	Marimo Notebook	Rendered HTML

Projects

Module	Editable Version	Readable Version
Midterm Project	Marimo Notebook	Rendered HTML
Capstone Project	Marimo Notebook	Rendered HTML

Installation

To download and edit a local copy of this project, follow this steps:

# Clone the repository
git clone https://github.com/elcapo/ml-zoomcamp-notes-and-homework
cd ml-zoomcamp-notes-and-homework

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

This will prepare an environment with all the dependencies needed to run all the notebooks, both for notes and homework.

Edit the Notebooks

Additionally, you may want to know these commands, which are useful for editing the notebooks and publishing changes

# Edit the notebooks
marimo edit

# Publish the changes
./export.sh

Note that this way of publishing the changes (saving the results as standalone HTML files) is slow, as all the code from each notebook has to be executed.

As a more efficient alternative, you can use the "Download HTML" manual option whenever you finish editing a notebook.

Special Environments

For certain projects, additional virtual environments are recommended.

REST API of Module 5

To run the REST API implemented in module 5 that serves the model, these steps are recommended:

# Change to the project folder
cd module-5/

# Deactivate any active virtual environment
deactivate

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the model
python -m model_package.api

This will run the model "naked". If you want to run it in the context of a Docker container, we should do:

docker build -t ml-zoomcamp .
docker run -d -p 5000:5000 -it ml-zoomcamp

Capstone

The instructions to run the capstone project are in its corresponding notebook.