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Essential Machine Learning Algorithms (with Python and R Codes)

December 5, 2017

All credit for the content goes to Sunil Ray & www.analyticsvidya.com.

Occasionally you come across something that is so useful that you have to share it with everyone!  This page is precisely that...

 

Sunil Ray via www.analyticsvidya.com has compiled a list of the most commonly used Machine Learning algorithms, provided nice and concise explanations (with images) of what they are and how they work - and then provides code snippets for both Python & R showing how to implement them.

 

This is perfect for those looking to broaden their knowledge of Machine Learning, as well as those who know either Python or R but want to learn or sharpen the other language.

 

The Machine Learning algorithms covered are as follows:

  • Linear Regression

  • Logistic Regression

  • Decision Tree

  • SVM

  • Naive Bayes

  • KNN

  • K-Means

  • Random Forest

  • Dimensionality Reduction Algorithms

  • Gradient Boosting algorithms

    • GBM

    • XGBoost

    • LightGBM

    • CatBoost

 

 

To whet your appetite, I've included below I've included an example screenshot of what you can expect for each of the algorithms.  Please do visit the page, I'm sure you'll learn something!

 

 

 

 

All credit for the content goes to Sunil Ray & www.analyticsvidya.com

 

 

 

 

 

 

 

 

 

 

 

 

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