10 Must-Know Machine Learning Algorithms for Data Scientists

There are many machinelearning algorithms that data scientists should know, but here are 10 that are particularly important:

1. Linear regression:

 

This algorithm is used to predict a continuous dependent variable based on one or more independent variables. It does this by fitting a line to the data that limits the amount of the squared contrasts between the anticipated qualities and the real qualities.

2. Logistic regression:

 

This algorithm is used to predict a binary outcome (e.g. 1 or 0, yes or no) based on one or more independent variables. It does this by fitting a curve to the data that separates the data into two classes.

3. Decision trees:

 

These algorithms create a tree-like model of decisions based on features of the data. Each interior hub in the tree addresses a choice in view of the worth of an input feature, and each leaf node represents a prediction.

4. SVM (support vector machines):

 

This algorithm is used for both classification and regression. In classification, it finds the hyperplane in an N-dimensional space that maximally separates the two classes. In regression, it finds the line or curve that best fits the data.

5. K-means clustering:

 

This is an unsupervised learning algorithm that groups similar data points together into clusters. It does this by iteratively assigning each data point to the cluster with the nearest mean, and then updating the mean of each cluster based on the data points it contains.

6. KNN (k-nearest neighbors):

 

This algorithm is used for both classification and regression. In classification, it predicts the value of a target variable based on the values of its k nearest neighbors. In regression, it predicts the value of a target variable based on the average value of its k nearest neighbors.

7. Naive Bayes:

 

This algorithm is used for classification. It makes predictions based on the probability of an event occurring given certain conditions. It assumes that the presence or absence of a particular feature is independent of the presence or absence of any other feature.

8. Random forests:

 

These algorithms are used for both classification and regression. They work by creating a large number of decision trees, and then averaging the predictions of all of the trees to make a final prediction.

9. Gradient boosting:

 

This algorithm is used for both classification and regression. It works by sequentially adding weak models to an ensemble, and then using the errors of the previous models to improve the predictions of the next model.

10. Neural networks: These algorithms are inspired by the structure and function of the human brain. They consist of input layers, hidden layers, and output layers, and can be trained to recognize patterns and make predictions based on input data. They are often used for tasks like image and speech recognition.