There are lists of algorithms which can be utilized for machine learning purpose. However, in below I will divide the list in to two major sections. Section 1 is based upon what I have seen being used in the field (cannot claim that I have seen everything). Section 2 is based on other references.

## Section 1

Regression analysis, Bays and K-Means/kNN are very common in the practical world, especially in the Government. Sometimes they are utilized as stand alone and in other times, they are used in conjunction with each other or with other models.

- C4.5 (Decision Trees)
- k-Means (clustering)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- Regression Analysis (Linear/Multiple/Logistic)
- Bayesian Networks

Artificial Neural Networks (ANN) might become very popular in near future. I tried to implement this in some of my projects and still doing research on it as part of my PhD research. ANN has unlimited flexibility and prospect and can be utilized for dynamic Machine Learning Environment. However, not many places are using this algorithm as of yet. It is relatively complex to understand and to apply.

## Section 2

**In 2006**, the IEEE Conference on Data Mining identified the top 10 ML algorithms as

- C4.5 (Decision Trees)
- k-Means (clustering)
- Support Vector Machines (SVM)
- Apriori
- Expectation Maximization (EM)
- PageRank
- AdaBoost
- k-Nearest Neighbors (kNN)
- Naive Bayes
- Classification and Regression Tree (CART)

An answer to the Quora question, **in 2011**, lists the following as potential candidates or additions:

- Kernel Density Estimation and Non-parametric Bayes Classifier
- K-Means
- Kernel Principal Components Analysis
- Linear Regression
- Neighbors (Nearest, Farthest, Range, k, Classification)
- Non-Negative Matrix Factorization
- Support Vector Machines
- Dimensionality Reduction
- Fast Singular Value Decomposition
- Decision Tree
- Bootstapped SVM
- Gaussian Processes
- Logistic Regression
- Logit Boost
- Model Tree
- Naïve Bayes
- PLS
- Random Forest
- Ridge Regression
- Support Vector Machine
- Attribute importance: MDL
- Anomaly detection: one-class SVM
- Clustering: k-means, orthogonal partitioning
- Association: A Priori
- Feature extraction: NNMF

And **a 2015 answer** provides the following:

- Linear regression
- Logistic regression
- k-means
- SVMs
- Random Forests
- Matrix Factorization/SVD
- Gradient Boosted Decision Trees/Machines
- Naive Bayes
- Artificial Neural Networks
- For the last one I’d let you pick one of the following:
- Bayesian Networks
- Elastic Nets
- Any other clustering algo besides k-means
- LDA
- Conditional Random Fields
- HDPs or other Bayesian non-parametric model

Fe other algorithms developed or re-developed at the Data Science Central’s research lab:

- Jackknife regression
- Feature extraction / selection (mentioned above, but this version is very different)
- Hidden decision trees
- Indexation and tagging algorithms

[Ref: http://www.datasciencecentral.com/]

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