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 1 | # Machine Learning with R
 2 | A collection of visualizations for machine learning with R, created in RMarkdown and exported as HTML. Applicable algorithms, descriptions of use cases for each algorithm, and applicable R libraries are listed below.
 3 | 
 4 | > **"A breakthrough in machine learning would be worth ten Microsofts."** - _Bill Gates_
 5 | 
 6 | > **"Machine learning is the next Internet."** - _Tony Tether, former director of DARPA_
 7 | 
 8 | > **"They call me Dirichlet because all of my potential is latent and awaiting allocation."** - [@ML_Hipster](http://www.twitter.com/ML_Hipster)
 9 | 
10 | -----
11 | ## Supervised Learning Algorithms
12 | ### Classification
13 | * Nearest Neighbor (kNN)
14 | * Naive Bayes
15 | * Decision Trees
16 | * Classification Rule Learners
17 | 
18 | ### Numeric Prediction
19 | * Linear Regression
20 | * Regression Trees
21 | 
22 | ### Multiple Uses
23 | * Artificial Neural Networks
24 | * Support Vector Machines
25 | 
26 | ## Unsupervised Learning Algorithms
27 | ### Pattern Detection
28 | * Association Rules
29 | 
30 | ### Clustering
31 | * k-means clustering
32 | 
33 | ## Meta-Learning Algorithms
34 | * Bagging
35 | * Boosting
36 | * Random Forests
37 | 
38 | -----
39 | ## R Libraries
40 | `class`: functions for classification, including k-nearest neighbor, learning vector quantization, and self-organizing maps
41 | 
42 | `gmodels`: programming tools for model fitting.
43 | 
44 | `tm`: framework for text mining applications.
45 | 
46 | `SnowballC`: interface to the C libstemmer library that implements Porter's word stemming algorithm for collapsing words to a common root to aid comparison of vocabulary.
47 | 
48 | `e071`: functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged cluster, and naive Bayes. Whew!
49 | 
50 | `klaR`: classification and visualization functions.
51 | 
52 | `C50`: C5.0 decision trees and rule-based models for pattern recognition.
53 | 
54 | `RWeka`: machine learning algorithms for data mining tasks, written in Java. Supports pre-processing, classification, regression, clustering, association rules, and visualization.
55 | 
56 | `psych`: graphical displays of path diagrams, factor analysis, and structural equation models.
57 | 
58 | `caret`: set of functions that attempt to streamline the process for creating predictive models.
59 | 
60 | `MXNetR`: wrapper for MXNet; for GPU computation on deep learning problems.
61 | 


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