A-Complete-Guide-to-Machine-Learning-Models

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A-Complete-Guide-to-Machine-Learning-Models

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Beginner friendly tutorial notebooks on traiditional machine learning models.

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# A Complete Guide to Machine Learning Models

This repository contains **beginner friendly tutorial notebooks** on traiditional machine learning models.

Each notebook consists of:

- an **introudction** to the model
- an **intuitive explanation** (easy to understand) on how the model works
- a **detailed explanation** (with diagrams, formulas ect.) on how the model works
- a **coding example** demonstrating how you can implement the model to solve a problem

## List of Notebooks (to be continued...)

- **`A Complete Guide to Linear Regression`**

- Linear Regression

- **`A Complete Guide to Regularized Regressions`**

- Lasso (R1) Regression
  - Ridge (R2) Regression
  - Elastic-Net Regression

- **`A Complete Guide to Logistic Regression`**

- Logistic Regression
 
- **`A Complete Guide to Decision Trees and Ensemble Methods`**

- Decision Trees
  - Random Forest
  - Gradient Boosting

- **`A Complete Guide to Boosting Methods`**

- AdaBoost
  - Gradient Boosting
  - LightGBM
  - XGBoost
  - CatBoost

- **`A Complete Guide to K-Nearest Neighbours`**

- K-Nearest Neighbour

- **`A Complete Guide to Naive Bayes`**

- Naive Bayes Classifier

- **`A Complete Guide to Support Vector Machine`**

- Support Vector Machine

- **`A Complete Guide to Clustering Methods`**

- K-Means Clustering
  - Hierarchical Clustering

- **`A Complete Guide to Dimension Reduction with PCA`**

- Principal Component Analysis

A-Complete-Guide-to-Machine-Learning-Models

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