100 Plus Machine Learning Algorithm

In this article, we are trying to cover 100 plus algorithm which is divided into 13 plus subcategories so let's understand which are those algorithms.

Let’s Have a look at 13 subcategories and those are:

1.Clustering,

2.Dimensionality Reduction,

3.Regression,

4.Decision Tree,

5.Bayesian,

6.Regulirizarion,

7.Reinforcement Learning,

8.Ensemble,

9.ANNs,

10.ARL,

11.Instance-Based,

12.Deep Learning,

13.Rule system,

14 and Other ML Algorithm.

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1.Clustering

Cluster analysis?or?clustering?is the task of grouping a set of objects in such a way that objects in the same group (called a?cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of?exploratory data analysis, and a common technique for?statistical?data analysis, used in many fields, including?pattern recognition,?image analysis,?information retrieval,?bioinformatics,?data compression,?computer graphics, and?machine learning. (Source Wikipedia)

1.????K Means

2.????K Medians

3.????BIRCH

4.????Fuzzy C-Means

5.????Fuzzy K-Modes

6.????Mini Batch K-Means

7.????DBSCAN

8.????Fuzzy Clustering

9.????Mean-Shift

10.?Minimum Spanning Tree

11.?Hierarchical clustering

12.?Expectation

13.?Optics Algorithm

2.Dimensionality Reduction

Dimensionality reduction, or?dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its?intrinsic dimension. (Source Wikipedia).

1.?????PCA Algorithm

2.?????PCR Algorithm

3.?????PLSR Algorithm

4.?????LDA Algorithm

5.?????ICA Algorithm

6.?????NMF Algorithm

7.?????RDA Algorithm

8.?????MDA Algorithm

9.?????QDA Algorithm

10.?PLS-DA Algorithm

11.?CCA Algorithm

12.?QDA Algorithm

13.?MDS Algorithm

14.?Sammon Mapping

15.?Projection Pursuit

16.?Diffusion Map

3.Regression,

In?statistical modeling,?regression analysis?is a set of statistical processes for?estimating?the relationships between a?dependent variable?(often called the 'outcome variable') and one or more?independent variables?(often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is?linear regression, in which one finds the line (or a more complex?linear combination) that most closely fits the data according to a specific mathematical criterion. (Source Wikipedia)

1.????Regression

2.????Linear Regression

3.????Logistic Regression

4.????Stepwise Regression

5.????MARS Algorithm

6.????Ordinary Least Squares

7.????Locally estimated Scatterplot

8.????Smoothing

4.Decision Tree

A?decision tree?is a?decision support?tool that uses a?tree-like?model?of decisions and their possible consequences, including?chance?event outcomes, resource costs, and?utility. It is one way to display an?algorithm?that only contains conditional control statements. (Source Wikipedia)

1.????Decision Stump

2.????CHAID

3.????CHART

4.?????M5

5.????ID3

6.????C4.5?and C5.0

7.????Decision Tree

8.????Conditional Decision Tree

5.Bayesian

In?probability theory?and?statistics,?Bayes' theorem?(alternatively?Bayes' law?or?Bayes' rule; recently?Bayes–Price theorem[1]:44, 45, 46 and 67), named after the Reverend?Thomas Bayes, describes the?probability?of an?event, based on prior knowledge of conditions that might be related to the event. (Source Wikipedia)

1.????Bayesian Network

2.????Bayesian Belief Network

3.????Multinomial Na?ve Bayes

4.????Gaussian Na?ve Bayes

5.????AODE

6.????Na?ve Bayes

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6.Regulirizarion

In?mathematics,?statistics,?finance,[1]?computer science, particularly in?machine learning?and?inverse problems,?regularization?is the process of adding information to solve an?ill-posed problem?or to prevent?overfitting. (Source Wikipedia)

1.?????Ridge Regression

2.?????LASSO

3.?????LARS

4.?????Elastic Net

7.Reinforcement learning?

Reinforcement learning?(RL) is an area of?machine learning?concerned with how intelligent agents ought to take actions in an environment to maximize the notion of cumulative reward.?Reinforcement learning?is one of three basic?machine learning?paradigms, alongside supervised?learning?and unsupervised?learning. (Source Wikipedia)

1.????SARSA Algorithm

2.????DDPG Algorithm

3.????NAF Algorithm

4.????A3C Algorithm

5.????PPO Algorithm

6.????TRPO Algorithm

7.????Q Learning

8.????Deep Q-Network

9.?????Constructing Skill Tree

8.Ensemble

In?statistics?and?machine learning,?ensemble methods?use multiple learning algorithms to obtain better?predictive performance?than could be obtained from any of the constituent learning algorithms alone.[1][2][3]?Unlike a?statistical ensemble?in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives. . (Source Wikipedia)

1.????GB DT

2.????GBRT

3.????Boosting

4.????Bagging

5.????AdaBoost

6.????Random Forest

7.????Stacked

8.????Blending Algorithm

9.????Generalization

10.?Gradient Boosting

11.?Machines

9. ANNs

Artificial neural networks?(ANNs), usually simply called?neural networks?(NNs), are?computing systems?vaguely inspired by the?biological neural networks?that constitute animal?brains.

1.?????Perceptron

2.?????Back-propagation

3.?????MLP Algorithm

4.?????Descent

5.?????Hopfield Network

6.?????RBFN Algorithm

7.?????Stochastic Gradient

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10.ARL

Association rule learning?is a?rule-based machine learning?method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness

1.?????Apriori Algorithm

2.?????Eclat Algorithm

3.?????FP Algorithm

11.Instance-Based

In?machine learning,?instance-based learning?(sometimes called?memory-based learning[1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."

1.????K Nearest Neighbor

2.????Self Organizing Map

3.????Locally Weighted Learning

4.????Learning Vector Quantization

5.????Support Vactor Machines

12.Deep Learning

Deep learning?(also known as?deep structured learning) is part of a broader family of?machine learning?methods based on?artificial neural networks?with?representation learning. Learning can be?supervised,?semi-supervised?or?unsupervised.

1.?????CNN

2.?????RNNs

3.?????LSTMs

4.?????Stacked Audio

5.?????Encoders

6.?????Deep Boltzmann Machine

7.?????Deep Belief Networks

13.Rule System

Rule-based machine learning approaches include?learning classifier systems,[4]?association rule learning,[5]?artificial immune systems,[6], and any other method that relies on a set of rules, each covering contextual knowledge.

1.????Cubist

2.?????ZeroR

3.?????OneR

4.?????Ripper

14.Other ML Algorithm

Other ML Algorithm includes

1.?????CN2 Algorithm

2.?????ALOPEX Algorithm

3.?????FastICA Algorithm

4.?????Feature Selection Algorithm

5.?????Algorithm Accuracy

6.?????Forward-Backward Algorithm

7.?????Linde Buzo Gray Algorithm

8.?????Evaluation Algorithm

9.?????Performance Measures

10.?Optimizing Algorithm

11.?Local Outlier Factor

12.?Logical Learning Machine

13.?T Distributed Stochastic Neighbor Embedding

14.?Wake sleep Algorithm

15.?LogitBoost

16.?Space PCA

17.?Structured KNN

18.?WMA Algorithm

19.?GeneRec

20.?Leabra

21.?RProp

22.?Dynamic Time Warping

Hope this article will help you to understand the algorithm related to Machine Learning.

Happy Learning.