kwangyy/it1244-additional-materials

Additional Materials for IT1244.

IT1244-Additional-Materials

This repo consists of additional materials that I use for my teaching in IT1244.

Requirements

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.

Why are you doing this?

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.)

Are these materials compulsory to do? Will you go through these in class?

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!

Do I have to do this in order?

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:

1. You'll need to explain some of these models in your Q&A section - can you?
2. The models that are featured can be good enough!

How can I contribute to this repo?

1. Star it! It gives me more visibility and it also allows easy access for you.
2. 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!
3. Give feedback to prof about my efforts so that this could be integrated into the IT1244 curriculum itself :p

Version updates:

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

Credits to these datasets:

1. Water Potability
2. California Housing Dataset

Other credits have been given in their respective notebooks!

Any other questions?

You know where to find me. All the best in this module!