This is the repository for the course Python for DSAI at Asian Institute of Technology.
Some resource worth mentioning:
- 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
- Prerequisities/0 - Installation
- For setting up tools for the course
- Prerequisities/0 - Course Notations
- Understanding notations is the first step towards conquering math, so take a look and familiarized with it
- 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.
- 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