7 Machine Learning Algorithms You Can’t Miss

This list of machine learning algorithms is a good place to start your journey as a data scientist. You should be able to identify the most common models and use them in the right applications.

7 Machine Learning Algorithms You Can't Miss
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Data science is a growing and varied field, and your work as a data scientist can cover many tasks and goals. Learning which algorithms work best in varying scenarios will help you meet these disparate needs.

It’s virtually impossible to be an expert in every kind of machine learning model, but you should understand the most common ones. Here are seven essential ML algorithms every data scientist should know.


Supervised Learning


Many companies prefer to use supervised learning models for their accuracy and straightforward real-world applications. While unsupervised learning is growing, supervised techniques are an excellent place to start as a data scientist.


1. Linear Regression


Linear regression is the most fundamental model for predicting values based on continuous variables. It assumes there’s a linear relationship between two variables and uses it to plot outcomes based on a given input.

Given the right data set, these models are easy to train and implement and relatively reliable. However, real-world relationships aren’t often linear, so it has limited relevance in many business applications. It also doesn’t manage outliers well, so it’s not ideal for large, varied data sets.


2. Logistic Regression


A similar but distinct machine learning algorithm you should know is logistic regression. Despite the similarity in name to linear regression, it’s a classification algorithm, not an estimation one. Whereas linear regression predicts a continuous value, logistic regression predicts the probability of data falling into a given category.

Logistic regression is common in predicting customer churn, forecasting weather and projecting product success rates. Like linear regression, it’s easy to implement and train but prone to overfitting and struggles with complex relationships.


3. Decision Trees


Decision trees are a fundamental model you can use for classification and regression. They split data into homogeneous groups and keep segmenting them into further categories.

Because decision trees work like flow charts, they’re ideal for complex decision-making or anomaly detection. Despite their relative simplicity, though, they can take time to train.


4. Naive Bayes


Naive Bayes is another simple yet effective classification algorithm. These models operate on Bayes’ Theorem, which determines conditional probability — the likelihood of an outcome based on similar occurrences in the past.

These models are popular in text-based and image classification. They may be too simplistic for real-world predictive analytics, but they’re excellent in these applications and handle large data sets well.


5. Random Forest


As you might guess from the name, random forests consist of multiple decision trees. Training each tree on randomized data and grouping the results lets these models produce more reliable results.

Random forests are more resistant to overfitting than decision trees and are more accurate in real-world applications. That reliability comes at a cost, though, as they can also be slow and require more computing resources.


Unsupervised Learning


Data scientists should also understand basic unsupervised learning models. These are some of the most popular of this less common but still important category.


6. K-Means Clustering


K-means clustering is one of the most popular unsupervised machine learning algorithms. These models classify data by grouping it into clusters based on their similarities.

K-means clustering is ideal for customer segmentation. That makes it valuable to businesses that want to refine marketing or speed onboarding, thus reducing their costs and churn rates in the process. It’s also useful for anomaly detection. However, it’s essential to standardize the data before feeding it to these algorithms.


7. Singular Value Decomposition


Singular value decomposition (SVD) models break complex data sets into easier-to-understand bits by separating them into their fundamental parts and removing redundant information.

Image compression and noise removal are some of the most popular applications for SVD. Considering how file sizes keep growing, those use cases will become increasingly valuable over time. However, building and applying these models can be time-consuming and complex.


Know These Machine Learning Algorithms


These seven machine learning algorithms are not an exhaustive list of what you may use as a data scientist. However, they are some of the most fundamental model types. Understanding these will help kickstart your career in data science and make it easier to comprehend other, more complex algorithms that build on these basics.

April Miller is managing editor of consumer technology at ReHack Magazine. She have a track record of creating quality content that drives traffic to the publications I work with.