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Python for DSAI

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
  1. Prerequisities/0 - Installation
  • For setting up tools for the course
  1. Prerequisities/0 - Course Notations
  • Understanding notations is the first step towards conquering math, so take a look and familiarized with it
  1. 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.
  1. 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:

I would also like to thank students who have contributed:

  • Akraradet Sinsamersuk
  • Pranisaa Charnparttarvanit
  • Chanapa Pananookooln

The course is structured into 5 big components:

1. Bootcamp: Python Basics

Focus on getting started.

  • Docker
  • Variables
  • List
  • Tuples, Dictionaries
  • Functions
  • Classes
  • Exception

2. Data Science

  • Numpy
  • Pandas
  • Matplotlib
  • Sklearn

3. Machine Learning from Scratch

  • Linear Regression
  • Logistic Regression
  • Naive Bayes

4. Signal Processing

  • Theories and preprocessing

5. Deep Learning

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

Appendix - Deployment

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

Appendix - Organization

  • How to organize your ML project folders


This is the repository for the course Python for Data Science covering Python Basics, Data Science Libraries (e.g., Pandas), ML from scratch, PyTorch, and Deployment.







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