10 Machine Learning Algorithms You Need to Know – Starweaver
10 Machine Learning Algorithms You Need to Know

10 Machine Learning Algorithms You Need to Know

We live in a revolutionized era filled with enormous computing power, data analytics, and cloud computing. Machine learning, which is based on algorithms, will have an essential role in the computing field. Machine learning algorithms are programs designed to improve the experience and learn from data without needing human intervention. You can use them for customer segmentation, spam detection, etc. Plus, with AI search algorithms, searching for things online has never been easier.

The machine learning sub-field has gained more popularity recently. This field helps predict or calculate suggestions using a large amount of data. Common examples of machine learning include Netflix's algorithms that suggest movies depending on the shows one has watched previously. Amazon algorithms also recommend books to customers based on what they have bought before.

Now, machine learning can positively impact your business in many ways. But, if you want to learn more about machine learning, one important question you'll come across is which type of machine learning algorithm is best for your project? Today, we will talk about ten different types of machine learning algorithms and what they're good for to help you make an informed decision.

But before we get started, we should mention that machine learning algorithms are categorized into three categories:


These algorithms are trained depending on labeled historical data usually annotated by humans to help predict future results. The training data set features inputs and expected outputs.


it uses unlabeled data, which algorithms will try making sense of by extracting patterns or rules independently. Algorithms will cluster data in various groups. There's no target outcome in this category.


These algorithms have been trained to make decisions. They'll use those decisions to prepare themselves based on an output's error/success. Then, by experience, they'll give accurate predictions. These algorithms usually learn which actions to take through trial and error.

Also, machine learning algorithms can be categorized depending on how they work and which problems they solve. Based on these, there are three categories: regression, classification, and clustering algorithms. That said, let's take a look at ten AI algorithms you need to know.

  1. Gradient Boosting

They are mostly used when handling high accuracy predictions of a massive amount of data. Gradient Boosting creates a robust algorithm using multiple weak algorithms. Rather than using one estimator, having several will create an accurate and robust algorithm. Gradient Boosting Algorithms include XGBoost that uses liner with tree algorithms, and LightGBM, which only uses tree algorithms. This algorithm is mostly used in data science competitions such as AV hackathon, Kaggle, etc. It can be used with Python as well as R Codes to give more accurate predictors.

  1. K-Means

It's been an unsupervised algorithm that solves clustering problems. It uses a particular procedure for forming clusters containing homogeneous data points. K value represents the algorithm's input, which means that it chooses k number of centroids. Then data sets next to a centroid combine with its centroid to create a cluster, and later a new centroid is created within collections. Data sets close to a new centroid then blend to expand its group. This process is repeated until centroids stop changing.

3. K-Nearest Neighbors (KNN)

It uses k nearest neighbors to predict new data points. The value of k is crucial for ensuring accurate prediction. By calculating distance using essential distance functions such as Euclidean, KNN determines the nearest. KNN stores available cases while classifying new topics by choosing a majority k neighbors votes. Then, the case will be placed into a class that's it's most common.

It's mostly used in analyzing activity like in credit card transactions. It needs high computation power, plus data should be initially normalized so that every data point is at the same range. Also, KNN might be computationally expensive.

  1. Naïve Bayes

It uses Bayes'' Probability Theorem; hence can only be used when features are not dependent on each other as required by Bayes'' Theorem. For instance, we can predict a flower type based on its width and length as these features aren't dependent. Note that, even if features are related, this algorithm will consider them independent when calculating particular outcomes'' probability. It's's often used when there are many classes in the problem.

  1. Support Vector Machine (SVM)

SVM is a classification algorithm that separates data points using a line. Raw data are plotted as points in an n-dimensional space where n represents how many features one has. The feature's value is then tied to a specific coordinate, which makes data classification easy. Classifiers can be used for splitting data and plotting them on a graph. SVM categorizes data into two different classes that indicate a distinction between these classes. SVM is used in various business applications like bioinformatics, image classifications, face detection, recognizing handwriting, etc.

  1. Decision Tree

This supervised algorithm is mostly used for problem classification. It's been a decision support tree that uses a tree graph or model decision and its possible consequences.

Decision Tree categorizes several sets of populations depending on some chosen parameters (independent variables) of that population. Some categorization techniques like entropy, Chi-square, etc. are used. It's mostly used for predictive modeling and marketing, which helps answer questions like which strategies are better?

  1. Random Forest

This is a collection of decision trees. Each tree estimates a classification that is known as "vote." Votes from every tree are considered, and the forest chooses classification with the most votes. Trees are planted and grown as follows:

If a training set has B cases, a sample of B cases is randomly taken, representing the training set for that growing tree.

If there are N number of input variables, n<

All trees are grown to the most extent; they can grow without pruning.

  1. Logistic Regression

It is used where individuals expect discreet outputs like some event occurring. Its predictions are discreet values after applying a transformation function, for example, whether it'll rain or not. By fitting data into a logic function, this algorithm helps predict the likelihood of an event. It uses some functions for squeezing values to fit into a specific range. The logic function is among functions used and has an "S" shape curve to classify binary where y= 0 or 1, 1 denoting the default class. Outputs usually take the form of the default class'' probability. Since it's a probability, the output ranges from 0-1.

  1. Dimensionality Reduction Algorithms

Data sets that contain multiple variables are usually hard to handle, especially nowadays, since there are many resources, data collecting in systems that occur at a more detailed level. As such, data sets will contain many variables which most of them are not necessary. Now, trying to identify variables that'll have a significant impact on your prediction in such cases will be impossible. That's where Dimensional Reduction Algorithms come in. It uses other algorithms like Decision Tree and Random Forest, to identify important variables.

  1. Linear Regression

It plots a line depending on the data set, i.e., the explanatory and dependent variables are plotted on the x and y-axis, respectively. Using data points, this algorithm will find a suitable fit line for the data. It uses an established dependent and independent variable's relationship to fit them to a line called regression and is represented by a linear equation; Y=a *X + b where Y represents the dependent variable, a slope, X independent variable, and b intercept. Basic theories of calculus are applied to find the values of a & b coefficients.

There are two types of Linear Regression: Simple Linear Regression, which uses only one independent variable, and Multiple Linear Regression that uses several independent variables. This algorithm can be used to understand how price changes affect goods and services by comparing various prices against their sales, helping an enterprise make good pricing decisions.


Machine learning algorithms make problem-solving easier. They can study data and offer accurate predictions without human intervention. But with many machine learning algorithms, choosing which one to use can be frustrating. We advise that you consider your data's size, nature, and quality, how urgent your task is, what problems you want to solve, and the available computational time. This means that you have to try several algorithms for different problems first. Thankfully, this AI algorithms list consists of popular algorithms that would be suitable for everyone.


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