Top 8 Machine Learning Algorithms Explained In Less Than 1 Minute Each

Intro: It’s 2022 and machines are learning to be like humans. We are in a world of constant technological advancement where even though all manual operations are mechanized, our pursuit of putting computers on auto-pilot isn’t stopping anytime soon. The dream of recreating self-starting humans (or better!) could soon be realized with Machine Learning algorithms which come in a wide range of varieties nowadays, some of which assist computers in learning and becoming smarter.

For a technology that could easily predict what could happen in the future, here’s a look at some of the widely used Machine Learning algorithms.

Listed below are some of the most commonly used algorithms in Machine Learning.


  1. Support Vector Machine (SVM)
  2. K- Nearest Neighbor (K-NN)
  3. Linear Regression
  4. Decision Tree
  5. Random Forest
  6. Naive Bayes
  7. K-Means
  8. Logistic Regression


We’ll now take a detailed look at the above-mentioned algorithms.


  1. Support Vector Machine

With the SVM algorithm, you can classify data by plotting the raw data as dots in an n-dimensional space (where n is the number of features you have). Once each feature’s value is linked to a particular coordinate, the data may then be easily categorized. Classifier lines can be used to separate the data into groups and plot them on a graph.

  1. KNN

Problems involving classification and regression can both be solved using this approach. It appears that the solution to categorization issues is more frequently applied within the Data Science business. It is a straightforward algorithm that sorts new instances by getting the consent of at least k of its neighbors and then saves all of the existing cases. The case is then given to the class that it most closely resembles. This calculation is made using a distance function.

Before choosing the K Nearest Neighbors algorithm, the following must be taken into account:

  1. i) The computational cost of KNN is high.
  2. ii) Higher range variables should be standardized to prevent algorithm bias.

iii) Preprocessing of the data is still necessary.

  1. Linear Regression

Consider how you would organize a set of random wood logs in ascending weight order to comprehend how linear regression functions. The drawback is that you can’t weigh every log. By examining the log’s height and girth (visual inspection) and organizing them according to a combination of these observable factors, you must estimate its weight. This is how machine learning’s linear regression works.


By fitting them to a line, this procedure creates a link between independent and dependent variables. The linear equation Y=a*X+b represents this line, which is referred to as the regression line.


  1. Decision Tree

One of the most widely used machine learning algorithms nowadays is the decision tree method, which is a supervised learning technique used to categorize issues. It may effectively classify dependent variables that are categorical or continuous. This method divides the population into two or more homogeneous sets based on the most significant traits or independent variables.


  1. Random Forest

Each tree is assigned a class, and the tree “votes” for that class, to categorize a new item based on its characteristics. The forest chooses the classification with the most votes (over all the trees in the forest).

Every tree is cultivated & planted as follows:

  1. i) If the training set has N cases, then a random sample of N cases is chosen. This sample will serve as the tree’s training set.
  2. ii) If there are M input variables, m is kept constant throughout this operation.

iii) Each tree is developed to its fullest potential. Pruning is not done.


  1. Naive Baye’s

A Naive Bayes classifier makes the underlying premise that the presence of one feature in a class does not influence the presence of any additional features. A Naive Bayes classifier would take into account each of these characteristics individually when determining the likelihood of a specific result, even if these attributes are related to one another. Despite being basic, it is known to perform better than even the most complex categorization techniques.


  1. K-Means Clustering

It is a clustering problem-solving unsupervised learning technique. Data sets are divided into a certain number of clusters, in such a way that each cluster’s data points are homogenous and distinct from those in the other groups.

The process of K-means cluster formation is as follows:

  1. i) For each cluster, the K-means algorithm selects k centroids, or points.
  2. ii) With the nearest centroids, or K clusters, each data point forms a cluster.

iii) New centroids are produced depending on the cluster members already present.

  1. iv) The closest distance for each data point is calculated using these updated centroids. v) Till the centroids stay the same, this process is repeated.


  1. Logistic Regression

To estimate discrete values (often binary values like 0/1) from a set of independent variables, logistic regression is utilized. Adjusting the data to a logit function, aids in predicting the likelihood of an event. Additionally known as logit regression.



The most commonly used Machine Learning Algorithms are discussed in this article. Machine Learning engineers are the most sought-after by top product-based companies. They are also paid well. But where can a candidate upskill himself in the field of Machine Learning? There are many institutes in our country that help candidates to upskill themselves in Machine Learning. But at SkillSlash, candidates are given 1:1 mentorship. Skillslash also has in store, exclusive courses like Data Science Course In Chandigarh, Full Stack Developer Course in Mumbai and Data structures course  to ensure aspirants of each domain have a great learning journey and a secure future in these fields. To find out how you can make a career in the IT and tech field with Skillslash, contact the student support team to know more about the course and institute.

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