Did you know the Machine Learning Algorithms and its various fields of working?

Created date: 25-09-2020

Created by : Annamalai

Top Machine Learning Algorithms:

  1. Naïve Bayes Classifier Algorithm

  2. K Means Clustering Algorithm

  3. Support Vector Machine Algorithm

  4. Apriori Algorithm

  5. Linear Regression Algorithm

  6. Logistic Regression Algorithm

  7. Decision Trees Algorithm

  8. Random Forests Algorithm

  9. K Nearest Neighbours Algorithm

  10. Artificial Neural Networks Algorithm

- Naive Bayes algorithm:

It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature

- K Means Clustering Algorithm:

Kmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. ... Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn't changing.

- SVM algorithms:

“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. ... Support Vectors are simply the co-ordinates of individual observation.

- What is Apriori algorithm with example?

What is the Apriori Algorithm? Apriori algorithm, a classic algorithm, is useful in mining frequent itemsets and relevant association rules. Usually, you operate this algorithm on a database containing a large number of transactions. One such example is the items customers buy at a supermarket.

- Linear Regression Algorithm :

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. ... So, this regression technique finds out a linear relationship between x (input) and y(output).

- Logistic Regression Algorithm :

Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. ... The hypothesis of logistic regression tends it to limit the cost function between 0 and 1.

- Decision Trees Algorithm :

Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too. ... The decision tree algorithm tries to solve the problem, by using tree representation.

- Random Forests Algorithm :

The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

- K Nearest Neighbours Algorithm:

In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.

- Artificial Neural Networks Algorithm :

AILabPage defines – Artificial neural networks (ANNs) as “Biologically inspired computing code with the number of simple, highly interconnected processing elements for simulating (only an attempt) human brain working & to process information model”. ... There are several kinds of Neural Networks in deep learning.

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