With technology getting more accessible and smarter, there has been a rise in the idea of Artificial Intelligence (AI) and Machine Learning (ML). There are many individuals – and even companies – that have become inspired by ML, since people are looking for ways to get more consumer attention, learn what works and what doesn’t work, how to improve oneself, and so on.

Yes, ML gives individuals and companies the power to compute according to the numbers and algorithms. With algorithms and the numbers considered “*the brain*” of ML, there’s absolutely no doubt that all data analysts should know it from the inside and out.

With that in mind, we will show you 7 of the most popular ML algorithms that all data analysts must learn.

## Decision Tree

“Data analysts should know about ML decision trees,” says Terry Wise, a tech journalist at Britstudent and Write My X.

“Decision trees are used for classifying and categorizing problems by dividing a set into any number of categories. Such categories are based on selected variables, and can generate many possibilities in that set. As such, the set becomes like a *‘tree*,’ thus showing the functionality of an informative process, and improving decision making in Machine Learning.”

## Linear Regression

Linear regression is a predictive model used in statistical analysis.

This algorithm works by making predictions based on a series of dependent and independent variables. From there, the relationship can be figured out by combining said variables. As time goes on, the algorithm will grow to recognize any disturbances (*or what’s known as “noise”*) and or correlated variables that might interfere with your results.

This is beneficial, if you want the inaccuracies to be weeded out.

## Random Forest

This next technique is like – so to speak – a makeshift “*forest*.”

When you have more than one decision tree to work with in your data analysis, then you have yourself a random forest. This algorithm takes multiple decision trees from a dataset, and then randomly assigns variable subsets to each stage of each tree.

Since this process is randomized, you’ll get instant insights as decision trees are produced and then brute-forced into generating results. However, it all depends on the value of each decision tree.

## Support Vector Machines

Support Vector Machines – or SVMs – are a form of classification, since they take training datasets, and then turn them into higher dimensions. Afterwards, they’re inspected for the optimal separation boundary(ies) – called hyperplanes – between classes.

Hyperplanes are pinpointed by locating support vectors. Support vectors exist when classes and their margins are made parallel to the hyperplane that’s defined by the shortest distance between that and its support vectors, thus classifying both linear and nonlinear data. In short, hyperplanes are calculated like this (*given that the data is linearly-separable*): **W . X + b = 0 **

Here’s how the formula works:

is a vector of weights*W*is a scalar bias, AND*b*are the training data*X*

Using this formula, you’ll be able to find the maximum-margin hyperplane (*or one that is only the same distance from the support vectors*). Simply put, when you combine the linear inequalities into a single equation, not only will you turn said inequalities into a constrained quadratic optimization problem, you’ll also set conditions based on Karush-Kuhn-Tucker.

## Bagging

“*Bagging** is about building a series of models, reading their results, and then choosing the right result*,” says Chris Davis, a tech writer at Phdkingdom and 1day2write.

“*When you chain or group classifiers together through voting, weighting, or combining, you’re looking for the most accurate classifier possible in ML. It’s basically called ‘**bootstrap aggregation**,’ because it studies and chooses samples, thus aggregating in ML*.”

## Apriori

You might or might not have heard about Apriori yet this algorithm has been growing in popularity over the recent years. Typically, you’ll find these algorithms in regular market analysis, which shows you product combinations that frequent databases.

How does it work?

The Apriori algorithm takes two data points, and then identifies positive and negative correlations between those two products. This allows company departments – say a sales department – to identify and name links that are vital to business success.

In other words, you’ll be able to connect sales and consumers to each other, thus giving you a winning marketing strategy! If you invest in ML to help you boost your sales, then this algorithm is for you!

## K-Means Clustering

Finally, K-means clustering!

This algorithm is taking divergent datasets, and then finding a class for the data that you’ve received from the divergence.

When you take K-means into consideration, data sets can be grouped into homogenous clusters. From there, you can use these ML algorithms to cluster and split the data into a certain number of points per cluster. Afterwards, ML will make sure the data is re-analyzed.

Once the data is analyzed again, new clusters will form into closer values.

## Conclusion

Machine Learning is here to stay and in the high-tech world – *courtesy of 2021* – Machine Learning is growing more powerful by the day. As a result, ML is becoming more accurate.

As you take into account these 7 essential ML algorithms, you will learn the ins and outs of ML, and then reap the rewards of knowing the numbers, marketing correctly, and making stronger business decisions in the future.

With Machine Learning, possibilities are imminent.

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*George J. Newton is a writer and editor for **Assignment writing services** and **Essay help**. He also contributes his work to websites such as **Coursework Help**. *