mlpractical

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #typescript #go #machine-learning #linux #nodejs #java #cli #kubernetes #vue #mcp #openai

(★ 19)

Machine Learning Practical Course Code Repository

mlpractical Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Machine Learning Practical

This repository contains the course materials for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning Practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).

This assignment-based course focuses on the implementation and evaluation of machine learning systems. Students will gain hands-on experience in designing, implementing, training, and evaluating machine learning systems through practical exercises and assignments.

## Repository Structure

The code in this repository is organized as follows:

* **`mlp` package**: A [NumPy](http://www.numpy.org/)-based neural network library designed specifically for this course. Students will implement parts of this package and extend it during labs and assignments.
* **`notebooks` directory**: A collection of [Jupyter](http://jupyter.org/) notebooks containing explanatory material and hands-on coding exercises for the course labs.

## Remote Working

If you are working remotely, please follow the instructions in our [remote working guide](notes/remote-working-guide.md).

## Getting Started

### Environment Setup

Before you can work with the course materials, you'll need to set up your Python development environment. This includes:

- Installing Python and required packages (NumPy, Jupyter, matplotlib, etc.)
- Setting up the course repository
- Configuring Jupyter notebooks
- Verifying your installation

**📋 Complete setup instructions**: Please follow the step-by-step guide in [`notes/environment-set-up.md`](notes/environment-set-up.md). This guide covers installation for different operating systems and provides troubleshooting tips.

All students will configure their environment during the first lab session with assistance from teaching staff.

## Working with the Exercises

**New to Jupyter notebooks?** Start with `notebooks/00_notebook.ipynb` to familiarize yourself with the Jupyter interface and features.

**Getting started with lab exercises:**
1. Navigate to the `notebooks` directory
2. For lab 1, work with notebooks starting with prefix `01`
3. Continue with subsequent numbered notebooks for each lab session

mlpractical

mlpractical Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

mlpractical

mlpractical Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README