This is the repository for the course Python for DSAI at Asian Institute of Technology.

Some resource worth mentioning:

1. Prerequisites/0 - Reading Roadmap

• For those who wants to know what papers to read. I have listed ONLY the most important papers you need to read in the field of machine learning

2. Prerequisities/0 - Installation

• For setting up tools for the course

3. Prerequisities/0 - Course Notations

• Understanding notations is the first step towards conquering math, so take a look and familiarized with it

4. Syllabus/0. Course Introduction.ipynb

• Contains how I run the course. This course is a 15 weeks course, each week having two labs of 3 hours each. Each lab always end with the assessment and solution.

5. I have put many folder titled "further-study" or "exercise". These resources are especially aimed for those who have completed the basic materials of the course, and would like to further improve your knowledge. The only reason I could not teach is due to time constraint of the course.

I would also like to give credits to several githubs that I have revised to create this:

• https://github.com/SethHWeidman/DLFS_code
• https://github.com/jakevdp/PythonDataScienceHandbook
• https://github.com/bentrevett/pytorch-sentiment-analysis

I would also like to thank students who have contributed:

• Akraradet Sinsamersuk
• Pranisaa Charnparttarvanit
• Chanapa Pananookooln

The course is structured into 3 big components, mostly focusing on preprocessing and modeling perspectives:

Focus on getting started.

• Python
• Numpy
• Pandas
• Matplotlib
• Sklearn

• Regression
• Classification
• Clustering
• Dimensionality Reduction

• Linear Regression
• Logistic Regression
• Convolutional Neural Network
• Long Short-Term Memory

• FastAPI + Docker
• Heroku + Github Actions
• Prometheus + Grafana
• AWS EC2