https://github.com/DCS-training/intromachinelearning
This course is aimed at providing an introduction to machine learning for those with some beginner level python/Rstudio skills. Go to the readme file
https://github.com/DCS-training/intromachinelearning
data-analysis data-wrangling machine-learning python statistics
Last synced: about 1 month ago
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This course is aimed at providing an introduction to machine learning for those with some beginner level python/Rstudio skills. Go to the readme file
- Host: GitHub
- URL: https://github.com/DCS-training/intromachinelearning
- Owner: DCS-training
- License: other
- Created: 2024-04-05T17:01:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-19T17:06:11.000Z (about 2 months ago)
- Last Synced: 2025-04-19T20:12:16.731Z (about 2 months ago)
- Topics: data-analysis, data-wrangling, machine-learning, python, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 9.21 MB
- Stars: 2
- Watchers: 0
- Forks: 2
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: License.md
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README
# Introduction to Machine Learning
Welcome to the repository for the An Introduction to Machine Learning course (formally 'Intro to Machine Learning in Python' delivered by in 2023 and 2024). This course is aimed at providing an introduction to machine learning covering:
- What Is Machine Learning and A Recap of Exploratory Data Analysis (EDA)
- Classification Basics and Logistic Regression
- More Classification Models (Decision Trees and k-NN)
- Regression and Practical Considerations in ML
This course will be available for participants to engage in either R or Python. It is therefore expected that those taking this course have some beginner level of either Python or R (as offered in the [Introduction to Python](https://github.com/DCS-training/IntroToPython?tab=readme-ov-file) or [Introduction to R](https://github.com/DCS-training/IntroToRAndRStudio) CDCS courses).
The materials in this repo are most recently adapted by **Chris Oldnall** (with original resources by **Bhargavi Ganesh**). All material collected here is free to use but is covered by a License: [](https://creativecommons.org/licenses/by-nc/4.0/) license
## How to use Noteable.
Throughout this course, we will be using the [Noteable](https://noteable.edina.ac.uk/) platform to run Jupyter notebooks and RStudio instances. This is a cloud-based computational notebook system that work on your browser from any device.
#### Start Noteable
1\. Open the following link in a new tab: [https://noteable.edina.ac.uk/login](https://noteable.edina.ac.uk/login).
2\. Login with your EASE credentials (either your Edinburgh university login, or those you were provided with).
3\. Start your environment
a. If you are using Python, select 'Standard Notebook (Python 3)' click 'Start'
b. If you are using RStudio, select 'RStudio' and click 'Start'
#### Download the files to Noteable Python
1. From the Noteable home page, click on the '+GitRepo' button at the top right of the screen.
2. In the 'Git Repository URL' field, copy the link to this GitHub repository, "https://github.com/DCS-training/intromachinelearning". Ignore all other fields.
3. Once filled in, click the 'clone' button. After a few moments, you will then see a new folder appear with the files.
#### Download the files to Noteable Rstudio
1. Go to File > New Project> Version Control > Git
2. Copy and paste this repository URL 'https://github.com/DCS-training/intromachinelearning' as the Repository URL
3. The Project directory name will be filled in automatically, but you can change it if you want your folder in Notable to have a different name
4. Decide where to locate the folder. By default, it will locate it in your home directory
5. Press Create Project
Congratulations, you have now pulled the content of the repository on your Notable server space! You are good to go for this course