We have prepared a set of recommended topics and resources to work through as you prepare for the programme. Some of you will come from a software engineering background and might be rusty on calculus; others might come from an academic background and be missing knowledge of Git, for example. Our prerequisites ensure that once the programme starts, you can focus on building your understanding of MLOps and Deep Learning rather than getting bogged down by any missing aspects of your foundational knowledge.

Foundational Knowledge

We don’t expect you to cover all of the materials listed here. However, it will slow your learning if you don’t have a good grasp of the fundamentals, so plan your preparatory study to cover a good chunk of any areas you are not familiar with:

• Shell: This short course by DataCamp is a good introduction - link
• Git & GitHub: Short course on YouTube - link
• SQL: DataCamp introduction to SQL - link.
• Docker: Introduction to Docker - link
• FastAPI: Introduction to FastAPI - link
• PostgreSQL: Creating a PostgreSQL database by DataCamp - link
• PyTorch: Introduction to Deep Learning with PyTorch - link
• Matplotlib: Introduction to matplotlib - link
• Pandas: Introduction to data manipulation with Pandas - link
• Computer Networking: Fun intro to Networking - link

Python

Python will be the primary language throughout the program. If you are not already comfortable programming in Python, here is a list of recommended learning materials:

• Python - link
• Data Visualisation - link
• Intro to Machine Learning - link