Learning Objectives

Here I will try to display my learning objectives for my specialization, and update them as I progress.

Machine Learning / AI
- Understanding the the difference between no-code/low-code AI tools, and developing and engineering actually AI.
- Understanding the different roles in the space – click to enlarge
- Followed ‘Data Alchemy’ course on skool.com by Dave Ebbelaar
  - Dave has been a data scientist for over a decade, and he have created a space for knowledge sharing and embarking new-comers to their AI journey!
React (Single Page Application)
- Looked up various sources to see popularity, support and community options around different SPA-frameworks, before deciding what I wanted to dive into.
  - https://www.spaceotechnologies.com/blog/single-page-application-frameworks/
  - https://medium.com/@Imaginovation/a-guide-to-the-top-10-web-application-frameworks-for-2023-2592b7c0349

Machine Learning / AI
- Setting up Anaconda (“The operating system for AI”) environments, installing packages, export and import environments
- Structure projects with concepts like cookiecutter
- Working with datasets responsibly – Data is immutal
React (Single Page Application)
- Setting up React dev-enviorement using Vite
- Code a working React application, installing and importing packages/modules
- Deploying a React-application at a hosting-provider (AWS) using own (sub)domain name
Development
- Git and GitHub version control for collaboration ease, code review, pull request and more
- Basic Python coding, use of python packages f.eg. ‘panda’ & ‘matplotlib’
- Use Jupiter Notebooks for interactive Python code execution, instead of full file execution
- Using AI while coding to improve efficiency and minimize errors, f.eg. GitHub CoPilot or Black formatter
- Working with different and/or isolated environments for each project, as they may differentiate due to (in)compatibility