Anomaly detection using Python ML libraries

Hi all, in this session we will do a deeper dive into using ML for anomaly detection (which we touched on in the last session).

We will focus on the practical steps of:
- getting the data ready
- feature engineering (what does, and does not make for good features)
- fitting an example model with DBSCAN
- local and global outliers and times when each will be interesting
If there's time, we will also have a quick tour of industries and settings where anomaly detection can be used.

Note: this will be a very practical session. Target audience will be those working in/around the ML Engineering discipline We'll save the maths and theory for another time 😅

Bring your laptop.
Bring a friend along!
When: Every other Wednesdays, 18:30-20:30
Where: Ground Floor, DeskLodge House, 2 Redcliffe Way, Redcliffe, Bristol BS1 6NL
We look forward in seeing you soon!