https://github.com/imperialcollegelondon/rcds-machine-learning-with-python
Getting started with scikit-learn for machine learning
https://github.com/imperialcollegelondon/rcds-machine-learning-with-python
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Repository
Getting started with scikit-learn for machine learning
Basic Info
- Host: GitHub
- Owner: ImperialCollegeLondon
- Language: Jupyter Notebook
- Default Branch: main
- Size: 169 MB
Statistics
- Stars: 29
- Watchers: 2
- Forks: 18
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Machine Learning with Python
Getting started with scikit-learn
Following on from the Introduction to Machine Learning course, this series of hands-on workshops will get you started with applying supervised and unsupervised machine learning methods in Python, using the popular scikit-learn package.
Intended Learning Outcomes
After completing this workshop, you will be better able to:
- Prepare a dataset for machine learning in Python
- Select a scikit-learn method appropriate for a particular learning task
- Construct your own workflows for model training and testing
- Evaluate the performance of a model
Setup
We will be working with python using jupyter notebooks. The easiest way to access jupyter is via the Anaconda platform.
Please install Anaconda from https://www.anaconda.com in advance of the first session.
Please ensure that you have an up-to-date scikit-learn package installed prior to starting the first session. General installation instructions are available here: https://scikit-learn.org/stable/install.html#installation-instructions
scikit-learn is part of the default installation of Anaconda, so you may already have everything you need.
Getting Started
Download this repository to your computer as a ZIP file and unpack it.
Open JupyterLab (within Anaconda) and navigate to the unpacked directory to work with the .ipynb notebooks.
Alternatively, you can run the notebooks online using Binder: 
Data sets
We will be working with a variety of real and synthetic data sets to illustrate various methods. For your own work between classes, you will be asked to identify a suitable data set from your own research or from other work within your field.
You can start thinking about this before the course, but the main requirements for a machine learning data set will be discussed more during the first session.
Owner
- Name: Imperial College London
- Login: ImperialCollegeLondon
- Kind: organization
- Email: icgithub-support@imperial.ac.uk
- Location: Imperial College London
- Repositories: 311
- Profile: https://github.com/ImperialCollegeLondon
Imperial College main code repository
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Last Year
- Watch event: 12
- Push event: 1
- Fork event: 8
- Create event: 1
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