---
# System prepended metadata

title: Data Mining
tags: [HK221]

---

# Data Mining
###### tags: `HK221`

## PCA
- Video
    1. [StatQuest: PCA main ideas in only 5 minutes!!!](https://www.youtube.com/watch?v=HMOI_lkzW08) (todo)
    2. [StatQuest: Principal Component Analysis (PCA), Step-by-Step](https://www.youtube.com/watch?v=FgakZw6K1QQ) (todo)
    3. [StatQuest: PCA in Python](https://www.youtube.com/watch?v=Lsue2gEM9D0)
    4. [StatQuest: PCA - Practical Tips](https://www.youtube.com/watch?v=oRvgq966yZg) (todo)
- Paper
    1. [Principal Component Analysis (phần 1/2)](https://machinelearningcoban.com/2017/06/15/pca/)
    2. [Principal Component Analysis (phần 2/2)](https://machinelearningcoban.com/2017/06/21/pca2/)

## t-sne
- Video
    1. [StatQuest: t-SNE, Clearly Explained](https://www.youtube.com/watch?v=NEaUSP4YerM) (todo)
    2. [Introduction to Machine Learning - 11 - Manifold learning and t-SNE](https://www.youtube.com/watch?v=MnRskV3NY1k) (todo)
    3. [t-SNE: Clearly Explained](https://www.youtube.com/watch?v=43ySR7_Yb4E)
    4. [t-SNE theory clearly explained | part -1](https://www.youtube.com/watch?v=efMDZEKnsiI)
- Paper
    1. [t-SNE clearly explained](https://towardsdatascience.com/t-sne-clearly-explained-d84c537f53a)
    2. [T-distributed Stochastic Neighbor Embedding](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)
    3. [How to Use t-SNE Effectively](https://distill.pub/2016/misread-tsne/)

## Github repo
1. [Dimensionality-Reduction-Techniques-on-MNIST-Dataset](https://github.com/FrozenFist27/Dimensionality-Reduction-Techniques-on-MNIST-Dataset/blob/main/MNIST_Dataset_Dimensionality_Reduction.ipynb)
2. [MNIST-Digit-Recognition](https://github.com/abhishekaanjana/MNIST-Digit-Recognition/blob/master/Digit%20Recognizer.ipynb)
3. [t-Distributed-Stochastic_Neighbour_Embedding-using-MNIST-dataset](https://github.com/RakeshRaj97/t-Distributed-Stochastic_Neighbour_Embedding-using-MNIST-dataset/blob/master/main.ipynb)