# A-List of the Top Machine Learning Algorithms in Python

Machine learning algorithms help data scientists to work on complex problems and solve them. If you are an aspiring Data Scientist, you need to have good knowledge of these algorithms too.

Letâ€™s start by understanding basic algorithm terminologies.

• Seen Data or Train Data: This is the information that we already have.
• Predicted Variable (Y): Machine Learning algorithms are aimed at predicting this variable.
• Features (X): Features or variable X is usually all the inputs that are fed to the system.

Here is a list of top Machine Learning Algorithms used by Data Scientists which can be applied to any problem with either Python or R code:

Watch this video to get a brief description of the below-mentioned Machine Learning Algorithms.

### Linear Regression

This algorithm is used to estimate real values on the basis of continuous values.

### Logistic Regression

This algorithm is used to estimate discrete values based on the given set of independent values.

### Decision Tree

This algorithm is mainly used for classification problems. A set of dependent variables are split into two or more homogeneous sets to arrive at a solution.

### SVM

This algorithm is another classification method in which a set of X variables are plotted on an n-dimensional space.

### Naive Bayes

This is a classification technique that is easy to build and very useful for large sets of data.

### kNN

This algorithm can be used for both, regression problems as well as for classifications.

### K-Means

This algorithm is used to solve clustering problems.

### Random Forest

The Random Forest is a term for an ensemble of trees. In this algorithm, there is a collection of decision trees that are used to classify new objects based on attributes.

### Dimensionality Reduction Algorithms

Data scientists collect heterogeneous data which from which they need to derive useful information. To do so they need the Dimensionality reduction algorithm along with other algorithms to sort out meaningful data from the rest.