# Top 5 Machine Learning Algorithms Every Data Scientist Should Know

Creating modern cutting edge projects involving artificial intelligence involves various steps. You have to take care about the hosting solutions to run your projects on, paying close attention to the hardware that will be used on your server.

For example, speaking of storage devices, an NVMe VPS server will be especially helpful in deploying machine learning solutions, since the latter require particularly high storage performance, which can be offered by NVMe drives, being several times faster than regular SSD drives.

Top 5 Machine Learning Algorithms Every Data Scientist Should Know

The kinds of machine learning algorithms is another thing you have to consider. Let’s have a look at some five machine learning algorithms that are good to know if you are going to deal with data science.

# Regression

Regression refers to a number of methods that belong to the supervised machine learning category. The main goal of regression methods is to build a prediction mechanism or explain certain values based on previous data.

Linear regression relies on a linear relationship between the input variables and the output variable, with the relationship being represented with a straight line and with hyperplane for multiple linear regression with multiple input variables.

An equation for a linear regression will look like this:

y=β0+β1x+ϵ

Where y stands for predicted output, β0 for the intercept of the line (value of y when x=0), β1 for the slope of the line (how much y changes for a unit change in x), and ϵ for the error term  (residuals) that capture the difference between the predicted and actual values.

The objective of regression is to find the best-fitting line (or hyperplane) through minimizing the sum of the squared differences between the predicted values and the actual values (the residuals).

Regression methods can be often used for different kinds of predictions, like predicting house prices or sales forecasting.

## Classification

Classification is another fundamental type of supervised learning. The goal of classification is to predict the categorical class labels of new instances based on past observations.

In contrast to regression, which is about predicting continuous values, classification predicts discrete outcomes (yes/no, human/ape).

The main underlying principle of classification is the labeling of the data where the outcome (or class) is known. The algorithm learns to distribute objects into classes, it can be two classes (spam/not spam) or many classes, like animal species.

The classification algorithms aim at finding a decision boundary that will allow them to figure out what belongs to one class and what belongs to another one.

Popular classification algorithms include

## Clustering

Unlike the previously described methods, clustering is already an unsupervised method that consists in grouping, i. e. clustering, objects with similar attributes. Clustering doesn’t rely on output information, with the algorithm defining the output on its own.

Clustering doesn’t rely on labeling; instead, it discovers the inherent structure within the data.

There are many popular clustering algorithms, including

Clustering is widely used for customer segmentation, document clustering, image segmentation, and market research.

## Dimensionality reduction

Dimensionality reduction is a machine learning algorithm that is used for reducing the number of input variables (features) of a data set while retaining as much of the relevant information as possible.

Since high-dimensional data can  be challenging to work with due to the "curse of dimensionality," requiring too many resources to process while the out can be also harder to interpret.

Dimensionality reduction is thus used to address these issues and simplify the data set, achieving easier analysis and modeling.

There are numerous popular dimensionality reduction techniques, including:

Dimensionality reduction is widely used for:

• Data visualization: since reducing the data to 2 or 3 dimensions allows for its easier visualization and interpretation.
• Noise reduction: Features of lesser importance are removed, which increases the model’s eventual performance.
• Features engineering: Creating new features that better capture the underlying patterns of the data.
• Speeding up algorithms: With the amount of features reduced, most algorithms will work faster, especially if they scale poorly with dimensionality.
• Data compression: While the significant information is kept, the amount of storage needed to store the data is considerably reduced.

## Ensemble methods

Ensemble methods are machine learning techniques that ensemble multiple models to get better results, with increased overall performance and robustness of predictions.

Ensemble methods are about combining weak learners, that is, models that perform a bit better than random guessing, to get stronger learners with better accuracy, stability, and generalizability.

Popular ensemble methods include Bagging (Bootstrap Aggregating), Random Forests, Boosting, AdaBoost (Adaptive Boosting), Gradient Boosting Machines (GBM), and Stacking (Stacked Generalization).

Ensemble methods can be efficiently used in finance, healthcare, marketing, e-commerce and competitions.

## To sum up

Now we’ve gone through some 5 widely used machine learning algorithms that you definitely have to know about before dealing with machine learning and artificial intelligence.

While there are many more further machine learning algorithms that will be great to know about, we hope that the above described 5 will provide you with a better idea of what machine learning algorithms are about, so it will be easier for you to dive deeper in the world of machine learning algorithms. Good luck!