OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
In this tutorial, we will complete a small end-to-end Machine Learning project using scikit-learn (scikit-learn.org), comprehensive, but simple and one of the most useful Machine Learning libraries for Python.
On a small dataset we will go through the typical pipeline of a real Machine Learning project: start with statistical summaries and visualization of the data, build multiple different machine learning models, use cross-validation to estimate their accuracies, select the best algorithm, make and evaluate the predictions on a validation set.
At the end of the session, we might have a look at the other useful functions integrated into scikit-learn.
The following tools will be used in this code clinic:
Python3 – www.python.org
Python SciPy libraries: – scipy – numpy – matplotlib – pandas – sklearn (shorten from scikit-learn)
You should stick to your favourite Python IDE; I will be working in Spyder – www.spyder-ide.org, which I highly recommend as IDE for R-users, who starts with Python and moves from R-Studio.
