These sessions will introduce the building blocks of the processes that are neededin a standard data-centric set-up and get everyone (interested) in the team involved.
- Introduction to Jupyter notebooks. Please clone the following repo: and install anaconda.org or intel distribution.
- Introduction to Python dictionaries, pandas/numpy
- Introduction to some basics of ML workflow and if time allows, we'll interactively explore one family of algorithms here. The original source that we will be following is Python Data Science Handbook, in the form of Jupyter notebooks, which you will be able to run on Colab
- Introduction to Feature Engineering and Model Validation
- Introduction to cross-validation, hyperparameters and hyperparameter tuning
- Visualisation using matplotlib/seaborn
Congratulations that you have made it so far! Now it’s time to put this knowledge into practice and build the an-to-end project!