What are algorithms in Data science?

To do Data Science, you should be familiar with the various Machine Learning algorithms used to solve numerous challenges, as no single algorithm can be the right choice for all types of use cases. All such algorithms are used in a variety of tasks obtained from the set of data under consideration, such as prediction, identification, clustering, and so on.

The following are by far the most widely Used Machine learning algorithms used by Data Scientists:

1. Linear Regression:
The linear regression method is used to predict the value of a dependent variable that uses the values of an independent variable.

2. Logistic Regression:
Linear Regression is often used to depict the connection between two continuous variables. Logistic Regression, on the other hand, works on discrete data.

3. Decision Trees:
Decision trees aid in the resolution of classification and prediction problems. It tends to make the information easier to understand, resulting in more accurate predictions. Almost every node in the Decision tree denotes a function or an attribute, each link signifies a decision, so each leaf node denotes a class label, i.e. the outcome.

There are a huge number of ML algorithms out there. Trying to classify them leads to the distinction being made in types of the training procedure, applications, the latest advances, and some of the standard algorithms used by ML scientists in their daily work. There is a lot to cover, and we shall proceed as given in the following listing:

  1. Statistical Algorithms

  2. Classification

  3. Regression

  4. Clustering

  5. Dimensionality Reduction

  6. Ensemble Algorithms

  7. Deep Learning

  8. Reinforcement Learning