What are the algorithms for machine learning

1.Linear Regression

Linear regression is probably one of the most widely known and understood algorithms in the field of statistics and machine learning.

2.Logistic Regression

Logistic regression is another technique that machine learning has borrowed from the field of statistics. It is the method of choice for binary classification problems.

3. Linear Discriminant Analysis

Logistic regression is a traditional classification algorithm whose usage scenario is limited to binary classification problems. If you have more than two classes, then Linear Discriminant Analysis algorithm (LDA) is the preferred linear classification technique.

4. Classification and regression trees

Decision trees are an important class of machine learning predictive modeling algorithms.

5. Plain Bayes

Plain Bayes is a simple but powerful predictive modeling algorithm.

6.K Nearest Neighbor Algorithm

K Nearest Neighbor (KNN) algorithm is very simple and effective.The model representation of KNN is the entire training dataset.

7. Learning Vector Quantization

One disadvantage of the KNN algorithm is that you need to deal with the entire training dataset.

8. Support Vector Machines

Support Vector Machines (SVMs) are probably one of the most popular and discussed machine learning algorithms today.

9. Bagging and Random Forests

Random Forests is one of the most popular and powerful machine learning algorithms, and it is an integrated machine learning algorithm.

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