This repo consists of additional materials that I use for my teaching in IT1244.
I do not expect my students to learn how to create their own environment. However, in the event you would like to replicate this, I have stated the requirements in requirements.txt
To download the requirements, simple use pip install -r requirements.txt.
I realise that people oftentimes do not know what they're doing in IT1244, and they require additional help. Therefore, this is something that I have decided to invest in.
In a way, this is also me trying to show off my Data Science knowledge to employers. It's a win-win. (Not really! But I believe in educating people, so I have decided to create my own materials.)
No - they are not. I will not go through these in class, but I will be willing to discuss anything from these supplementary materials if needed be!
For the most part, yes. These assignments presume the previous assignment(s) and the knowledge imparted from these. I assure you however, that I will make it as simple as possible.
You can, however, skip the additional models section if needed be especially because:
- You'll need to explain some of these models in your Q&A section - can you?
- The models that are featured can be good enough!
- Star it! It gives me more visibility and it also allows easy access for you.
- If you'd like to add to the repo, you can send me a message on my Telegram @foodfoundations (might change) or you can always contact me on Linkedin (will never change). We can discuss before you create any content!
- Give feedback to prof about my efforts so that this could be integrated into the IT1244 curriculum itself :p
V1.0 - Created 6 new lessons, Git, Pandas, KNN, Linear Regression, Logistic Regression, and K-Means Clustering.
V1.1 - Created additional lesson on tree-based models with decision trees, random forests and gradient boosting.
Credits to these datasets:
Other credits have been given in their respective notebooks!
You know where to find me. All the best in this module!