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veniversum/iaml2018

Analysis updated 2026-08-15 · repo last pushed 2018-10-12

Jupyter NotebookAudience · generalComplexity · 1/5DormantSetup · moderate

TLDR

University course materials for Introductory Applied Machine Learning at Edinburgh. Interactive Jupyter notebooks teach practical ML with Python, from setup basics through advanced topics.

Mindmap

mindmap
  root((repo))
    What it does
      Interactive ML labs
      Runnable notebook code
      Step-by-step setup guide
    Audience
      University students
      Distance learners
      ML beginners
    Tech stack
      Python
      Jupyter notebooks
      conda package manager
    Use cases
      Learn ML basics
      Complete coursework
      Practice with code
    Setup
      Install conda
      Create environment
      Download as zip option
    Notable features
      Beginner-friendly docs
      Windows troubleshooting
      No git required

Code map

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What do people build with it?

USE CASE 1

Follow along with university machine learning labs at your own pace

USE CASE 2

Learn Python-based ML concepts through interactive notebooks with runnable code

USE CASE 3

Set up a complete Python data science environment from scratch as a beginner

USE CASE 4

Complete introductory applied machine learning coursework assignments

What is it built with?

PythonJupyter Notebookconda

How does it compare?

veniversum/iaml2018agostynah/distributed-vector-memory-routingakashsingh3031/python-libraries
Stars0
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Last pushed2018-10-122020-12-03
MaintenanceDormantDormant
Setup difficultymoderatemoderateeasy
Complexity1/53/51/5
Audiencegeneralresearchervibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires installing conda and creating a Python environment, Windows users may hit path and environment variable issues that require troubleshooting.

No license information is provided in the repository, so usage rights are unclear.

In plain English

This repository contains the lab exercises and coursework materials for a university course called "Introductory Applied Machine Learning." It is designed for students at the University of Edinburgh, giving them everything they need to start learning practical machine learning using Python. The project itself is a collection of interactive Jupyter notebooks. These are documents that mix explanatory text with runnable code, allowing students to read a concept and immediately experiment with it. The README focuses on walking students through the setup process: installing a Python package manager called conda, creating an isolated software environment, and downloading the course materials. Once set up, students open the notebooks to work through the labs and assignments. The primary users are students enrolled in the course, as well as distance learners who want to follow along on their own computers. A concrete example of how this works is a student downloading the materials, opening the first notebook ("Lab 0: Introduction"), and running the provided code to learn the basics before moving on to more advanced topics. Distance learners must still submit their final assignments through the university's internal system. A notable aspect of this repository is its thorough, beginner-friendly approach to setup. The instructions assume the reader has little to no experience with command-line tools, carefully explaining concepts like software environments and system paths. It also includes extensive troubleshooting for common issues across different operating systems, particularly Windows. The README notes that Git is not taught as part of this course, so students can simply download a zip file of the materials if they are not familiar with version control.

Copy-paste prompts

Prompt 1
I want to learn introductory machine learning with Python. Walk me through setting up conda and creating an isolated environment, then help me open and run my first Jupyter notebook with basic ML code.
Prompt 2
I downloaded a zip of university ML lab notebooks but I'm on Windows and conda isn't working. Help me troubleshoot common Windows path and environment issues for Jupyter notebooks.
Prompt 3
I'm a distance learner working through applied ML labs in Jupyter notebooks. Help me understand the code in Lab 0 Introduction and explain what each section does so I can experiment with it.
Prompt 4
I have conda installed and my environment set up. Guide me through opening the first lab notebook and running the provided Python code step by step, explaining what each code cell does.
Prompt 5
I have no command-line experience. Explain what a software environment is, why I need conda, and help me create my first isolated Python environment for running Jupyter notebooks.

Frequently asked questions

What is iaml2018?

University course materials for Introductory Applied Machine Learning at Edinburgh. Interactive Jupyter notebooks teach practical ML with Python, from setup basics through advanced topics.

What language is iaml2018 written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, conda.

Is iaml2018 actively maintained?

Dormant — no commits in 2+ years (last push 2018-10-12).

What license does iaml2018 use?

No license information is provided in the repository, so usage rights are unclear.

How hard is iaml2018 to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is iaml2018 for?

Mainly general.

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