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.

Let’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 a n-dimensional space.
• Naive Bayes
This is a classification technique which 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 which are used to classify new objects based on attributes.
• Dimensionality Reduction Algorithms
Data scientists collect heterogeneous data which from which they need to derive usefulinformation. To do so they need the Dimensionality reduction algorithm along with other algorithms to sort out meaningful data from the rest.