Skip to content

DrewWham/Machine-Learning-Overview

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

41 Commits
 
 
 
 
 
 

Repository files navigation

Overview-of-Machine-Learning

Languages, Software Libraries and Packages

Base Languages

Data Wrangling and Transformation

Cluster Computing

Cloud Computing

Modeling

ML Algorithms


Supervised


Use cases: Prediction, classification and labeling, quntification of risk and uncertinty, feedback based recommendation Examples: Sales forecasting, Vegas odds, insurance risk, credit fraud detection

Regression

Classification

Feature Importance

Supervised Dimensionality Reduction

Unsupervised


Use cases: Structure Discovery, grouping/labeling when no labels are known, implicit recommendation, improve supervised methods Examples: Youtube Recomendations, google translate

Dimensionality Reduction

Non-Parametric Classification


Other Domains that are Interestingly Different

Use cases: A/B/C... tests, maintain equalibrium or setpoint, explore/exploit optimization Examples:Website optimization, content personalization, auto pilot

Autonomus Control/Decision Theory

Reinforcement Learning


Ensembles

Kaggle Ensambling Guide

  • Voting
  • Averaging
  • Stacked ensembling/Blending

Be careful when you ensamble, "Here there be dragons."


Evaluating Accuracy

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages