Originally published by in 2013, it still is a goldmine for all machine learning professionals. The algorithms are broken down in several categories. Here we provide a high-level summary, a much longer and detailed version can be found here. You can even download an algorithm map from the original article. Below is a much smaller version.

It would be interesting to list, for each algorithm,

- examples of real world applications,
- in which contexts it performs well,
- if it can be used as a black box,
- ease of use and interpretation,
- how it handles missing data,
- enterprise version available or not,
- integration with existing analytics platforms or real-time systems,
- constraints on data (e.g. Naive Bayes performs poorly on correlated variables),
- maintenance/scalability issues,
- distributed implementation,
- speed or computational complexity,
- can easily be blended with other algorithms

and generally speaking, compare these algorithms. I would add HDT, Jackknife regression, density estimation, attribution modeling (to optimize marketing mix), linkage (in fraud detection), indexation (to create taxonomies or for clustering large data sets consisting of text), bucketisation, and time series algorithms.

For more on machine learning (ML), click here.

*Ensemble methods to fit data: see original paper*

- Ordinary Least Squares Regression (OLSR)
- Linear Regression
- Logistic Regression
- Stepwise Regression
- Multivariate Adaptive Regression Splines (MARS)
- Locally Estimated Scatterplot Smoothing (LOESS)

- k-Nearest Neighbour (kNN)
- Learning Vector Quantization (LVQ)
- Self-Organizing Map (SOM)
- Locally Weighted Learning (LWL)

- Ridge Regression
- Least Absolute Shrinkage and Selection Operator (LASSO)
- Elastic Net
- Least-Angle Regression (LARS)

- Classification and Regression Tree (CART)
- Iterative Dichotomiser 3 (ID3)
- C4.5 and C5.0 (different versions of a powerful approach)
- Chi-squared Automatic Interaction Detection (CHAID)
- Decision Stump
- M5
- Conditional Decision Trees

- Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Averaged One-Dependence Estimators (AODE)
- Bayesian Belief Network (BBN)
- Bayesian Network (BN)

- k-Means
- k-Medians
- Expectation Maximisation (EM)
- Hierarchical Clustering

- Apriori algorithm
- Eclat algorithm

- Perceptron
- Back-Propagation
- Hopfield Network
- Radial Basis Function Network (RBFN)

- Deep Boltzmann Machine (DBM)
- Deep Belief Networks (DBN)
- Convolutional Neural Network (CNN)
- Stacked Auto-Encoders

- Principal Component Analysis (PCA)
- Principal Component Regression (PCR)
- Partial Least Squares Regression (PLSR)
- Sammon Mapping
- Multidimensional Scaling (MDS)
- Projection Pursuit
- Linear Discriminant Analysis (LDA)
- Mixture Discriminant Analysis (MDA)
- Quadratic Discriminant Analysis (QDA)
- Flexible Discriminant Analysis (FDA)

- Boosting
- Bootstrapped Aggregation (Bagging)
- AdaBoost
- Stacked Generalization (blending)
- Gradient Boosting Machines (GBM)
- Gradient Boosted Regression Trees (GBRT)
- Random Forest

- Computational intelligence (evolutionary algorithms, etc.)
- Computer Vision (CV)
- Natural Language Processing (NLP)
- Recommender Systems
- Reinforcement Learning
- Graphical Models

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## Anil kumar

Really helpful....

Oct 29, 2015

## Mahesh N Sanil

Its good and explained well, clustered algorithm. Love reading and understanding the clusters.

Dec 19, 2015

## Ben Dutta

I was hoping to see '(Multiple) Correspondence Analysis' under the list of "Dimensionality Reduction" methods.

Dec 29, 2015