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https://github.com/milaan9/machine_learning_algorithms_from_scratch

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
https://github.com/milaan9/machine_learning_algorithms_from_scratch

data-science decision-trees dynamic-time-warping error-functions fitting-algorithm frequentist-methods gaussian-naive-bayes machine-learning machine-learning-algorithms machine-learning-matlab machine-learning-python matlab4datascience naive-bayes-classifier python4datascience random-forest singular-value-decomposition tutor-milaan9 value-iteration-algorithm

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This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

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# Python_Machine_Learning_Algorithms_from_Scratch



## Introduction πŸ‘‹



This repository explores the variety of techniques anf alorithm commonly used in machine learning and the implementation in MATLAB and PYTHON.



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## Table of contents πŸ“‹



Alogrithm used are :

  1. Decision Trees and Random Forest Classifier

  2. Naive Bayes Classifier

  3. Gaussian Naive Bayes Calssifier

  4. Mixture Of Gaussians using EM Algorithm

  5. Neural Network

  6. Singular Value Decomposition

  7. Principal Component Analysis

  8. Fitting the data to a 1D Gaussian

  9. Fitting the data to a 2D Gaussian

  10. K Nearest Neighbours

  11. Linear Regression

  12. Logistic Regression

  13. K-Mean Clustering

  14. Value-Iteration-Method

  15. Dynamic Time Warping

  16. Error Function and Regularisation



These are online **read-only** versions. However you can **`Run β–Ά`**  all the codes **online** by clicking here ➞ binder



* Recommendation for ML Enthusiasts: **[Machine Learning A-Zβ„’: Hands-On Python & R In Data Science](https://www.udemy.com/machinelearning/)**

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## Frequently asked questions ❔



### How can I thank you for writing and sharing this tutorial? 🌷



You can Star Badge and Fork Badge Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.



Go [**`here`**](https://github.com/milaan9/Python_Machine_Learning) if you aren't here already and click ➞ **`✰ Star`** and **`β΅– Fork`** button in the top right corner. You will be asked to create a GitHub account if you don't already have one.



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### How can I read this tutorial without an Internet connection? GIF



1. Go [**`here`**](https://github.com/milaan9/Python_Machine_Learning) and click the big green ➞ **`Code`** button in the top right of the page, then click ➞ [**`Download ZIP`**](https://github.com/milaan9/Python_Machine_Learning/archive/refs/heads/main.zip).



    ![Download ZIP](img/dnld_rep.png)



2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

    

3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

  

    **`Kernel > Restart & Clear Output`**

    

This will clear all the outputs and now you can understand each statement and learn interactively.



If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.



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## Authors ✍️



I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcomeπŸ™



See [github's contributors page](https://github.com/milaan9/Python_Machine_Learning/graphs/contributors) for details.



If you have trouble with this tutorial please tell me about it by [Create an issue on GitHub](https://github.com/milaan9/Python_Machine_Learning/issues/new). and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.



If you like this tutorial, please [give it a ⭐ star](https://github.com/milaan9/Python_Machine_Learning).



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## Licence πŸ“œ



You may use this tutorial freely at your own risk. See [LICENSE](./LICENSE).

Copyright (c) 2020 Dr. Milan Parmar