> For the complete documentation index, see [llms.txt](https://tlr.gitbook.io/data-science/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tlr.gitbook.io/data-science/master.md).

# Welcome

## Table of Contents

* [Machine Learning Basic](https://tlr.gitbook.io/data-science/machine-learning-basics)
  * [Gradient Descent](https://tlr.gitbook.io/data-science/machine-learning-basics/gradient-descent)
  * [Cross Validation](https://tlr.gitbook.io/data-science/machine-learning-basics/cross-validation)
* [Supervised](https://tlr.gitbook.io/data-science/supervised-learning)
  * [Decision Tree](https://tlr.gitbook.io/data-science/supervised-learning/decision-tree)
  * [Linear Regression](https://tlr.gitbook.io/data-science/supervised-learning/linear-regression-coding-part-1)
* [Unsupervised](https://tlr.gitbook.io/data-science/unsupervised-learning)
  * [K-Means Clustering](https://tlr.gitbook.io/data-science/unsupervised-learning/k-means-clustering)
* [Neural Network](https://tlr.gitbook.io/data-science/neural-network)
  * [MNIST with CNN & FCL](https://tlr.gitbook.io/data-science/neural-network/mnist-with-cnn-and-fully-connected-layers)
  * [Neural Network](https://tlr.gitbook.io/data-science/neural-network/a-simple-neural-network)
  * [Perceptron](https://tlr.gitbook.io/data-science/neural-network/perceptron)
* [Projects](https://tlr.gitbook.io/data-science/projects)
  * [Stock Prediction with BERT](https://tlr.gitbook.io/data-science/projects/stock-prediction-with-bert-1)
  * [Text Generation](https://tlr.gitbook.io/data-science/projects/text-generation)
* [Data Science](https://tlr.gitbook.io/data-science/data-science)
  * [World Happiness Record](https://tlr.gitbook.io/data-science/data-science/world-happiness-record)

## To Do List

* Colorizing Gray Images
* Sudoku Solver from Screenshots (or pictures)
* Support Vector Machine
* Generative Discriminative Analysis (LDA, QDA)
* Text Summarization (Report)
* Find Waldo with Faster-RCNN, YOLOv3, and SSD (Report)
* Normalization vs Standardization
* Principal Component Analysis
* Multiple Linear Regression
* Simple EDA (Walkthrough)
* Types of Data Visualizations (matplotlib, seaborn, pyplot) 2D
*