README.md

Python_Machine_Learning_Algorithms_from_Scratch

Introduction 👋

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 📋

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 ▶ all the codes online by clicking here ➞

• Recommendation for ML Enthusiasts: Machine Learning A-Z™: Hands-On Python & R In Data Science

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Frequently asked questions ❔

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

You can and 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 if you aren't here already and click ➞ ✰ Star and ⵖ 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?

1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.

   

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 welcome🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. 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.

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Licence 📜

You may use this tutorial freely at your own risk. See LICENSE. Copyright (c) 2020 Dr. Milan Parmar