machine_learning_from_scratch

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machine_learning_from_scratch

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# Machine learning from scratch

## Introduction

In this repository, I will gradually implement some frequently used machine learning algorithms from scratch with Python. This would include both the intuitive of the algorithms and mathematics bebind. Just ignore the math part if it troubles you.

Machine learning or deep learning package such as sklearn, tensorflow etc will not be used. We will only use [Numpy](http://www.numpy.org/) to facilitate numerical computation. All texts will be written using English, if you find my English hard to understand, please check the Chinese repository-[中文版](https://github.com/chenxingwei/machine_learning_from_scrach_Chinese/tree/master).

## Algorithm Index

1. [Linear Regression](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/1.linearRegression.md)

> Codes: [Simple linear regression](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/simple_linear_regression.py), [Linear regression](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/linear_regression.py)

2. [Linear Regression with Gradient Descent](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/2.linearRegressionGradientDescent.md)

> Codes: [linear regression with gradient descent](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/linear_regression_gd.py)

3. [Logistic Regression](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/3.logisticRegression.md)
    > Codes: [Logistic regression with gradient descent](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/logistic_regression.py)

4. [Regularization (not finish in all details)](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/4.regularization.md)
    > Codes: [Linear regression with L2-norm regularization](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/linear_regression_l2.py), [Logistic regression with L2-norm regularization](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/logistic_regression_l2.py)

5. [KNN (K Nearest Neighbor)](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/5.KNN.md)
    
    > Codes: [K Nearest Neighbors](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/KNN.py)

6. [Softmax Regression](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/6.softmax.md)
    
    > Codes: [Softmax regression with gradient descent](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/softmax_regression.py)

7. [Kmeans clustering](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/7.kmeans.md)
    
    > Codes: [Kmeans](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/codes/kmeans.py)

8. [Naive Bayes](https://github.com/chenxingwei/machine_learning_from_scratch/blob/master/algorithm/8.naiveBayes.md)
    
    > Codes:

9. [Decision Tree-looking forward]()
    
    > Codes:

10. [Random Forest-looking forward]()
    
    > Codes:

11. [SVM-looking forward]()
    
    > Codes:

12. [PCA - looking forward]()

> Codes:

## Summary

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