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 newbies who have trouble installing Python and other tools
- 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 would also like to give credits to several githubs that I have revised to create this:
- https://github.com/drgona/Pytorch_bootcamp_Udemy
- https://github.com/SethHWeidman/DLFS_code
- https://github.com/jakevdp/PythonDataScienceHandbook
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
Focus on understanding the math + coding via coding from scratch
- Linear regression
- Polynomial regression
- Regularization
- Logistic regression
- Naive Gaussian
- Support Vector Machines
- Decision Trees
- K-Nearest Neighbors
- Bagging
- Random Forests
- Boosting - AdaBoost, Gradient Boosting
- K-means
- Gaussian Mixture Models
- Principal Component Analysis
- Manifold Learning
- Momentum
- Batch Norm
- Dropout
- Decay Learning Rate
- Glorot Initialization
- Activation Functions
- Basics
- ANN
- CNN
- RNN