In this session, you will be taken to an external environment to work through some code examples. Click on the link below and it will open up a Jupyter Notebook (in MyBinder). Note that it might take some time to instantiate the cloud environment when you first click so please be patient (this can be a useful time to go back and review some of the material we covered previously!). To keep your OpenLearnCreate session open, I suggest Ctrl+click on the link and opening in a new tab.

This section goes into detail on data science using Julia. We focus here on the considerations for applying machine learning to real-world problems such as in aquaculture. There is a huge volume of resources on the theory and algorithmic side of machine learning (some mentioned on the previous page). In this section, however, we cover concepts related to data merging, data transformation, algorithm selection, and performance evaluation. Machine Learning Yearning by Andrew Ng is an excellent (and free!) book that provides more detail on this topic. The book can be downloaded here (scroll to the bottom and enter your email).

Part 3 - Julia with Python/R for Data Science Live Tutorials

Note that if you prefer to work in your own local Julia environment, you can clone the code from here