|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Machine Learning Articles||519||4 years ago|
|Monthly Series - Top 10 Machine Learning Articles|
|100 Days Of Ml Code||201||4 months ago||Jupyter Notebook|
|A day to day plan for this challenge. Covers both theoritical and practical aspects|
|Awesome Ai||121||2 years ago||mit|
|The guide to master Artificial Intelligence (machine learning & deep learning) from beginner to advance|
|Free Artificial Intelligence Resources||49||2 years ago||n,ull||mit|
|Welcome, to this Open Source Repository regarding FREE ARTIFICIAL INTELLIGENCE RESOURCE. Get Benefit from the free resources mention & kindly five STAR & FORK this so that it can get maximum Fame so that Everyone can take advantage.|
|A Guide To Machine Learning In R||17||4 years ago||R|
|A series of articles to get started into the field of Machine Learning with R language|
|Statistical Learning Using R||16||5 years ago||R|
|This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.|
|Ml Classifier||10||17 days ago||4||mit||Jupyter Notebook|
|Classify news articles into different categories using Machine Learning|
|Trendster||5||6 years ago||Python|
|Galvanize Capstone Project Repo: demystifying the evolution of topics over time.|
|Source Code||4||5 years ago||Jupyter Notebook|
|The accompanying repository for all the source code of articles from the ML Endeavours blog. ML Endeavours is a blog dedicated to Machine Learning, a subset of Artificial Intelligence (AI).|
|Reproducibleresearchcode||2||4 years ago||mit||C|
|Python code to reproduce our article "Toward faultless content-based playlists generation for instrumentals"|
This is a Statistical Learning repository which will consist of various Learning algorithms and their implementation in R and their in depth interpretation. Below are the links to the implementation and their in-depth explanation of the learning algorithms in R. All the documents below contain the under-lying mathematical concepts explained with respect to a simple case study in R.
Model Selection techniques - AIC, BIC, Mallow's Cp , Adjusted R-squared , Cross validation error.
Shrinkage Methods and Regularization techniques - Ridge Regression , LASSO, L1 norm, L2 norm.
Non-linear Regression and parametric models
Non-parametric model - K-nearest neighbor algorithm
Tree based Modelling - Decision Trees
Bayesian Modelling technique : Naive Bayes algorithm.
Ensemble learning - Random Forests, Gradient Boosting , Bagging.
Re-sampling methods and Cross Validation