Are you looking for the top 7 machine learning algorithms for AI/ML aspirants in 2021?

We have answers.

Machine Learning is considered one of the most sought-after skills in the IT and software industry. The demand for Machine Learning Engineers and Analysts has seen a tremendous rise over the last few years.

According to Forbes, Machine learning algorithms that are popular in the field may soon replace several jobs- in computer science, manufacturing, transportation (self-driving cars), architecture, and healthcare. (Source)

Alongside the benefits that machine learning AI development can bring to the world is a considerable sum of money.

What is Machine Learning (ML)?

It is the study and building of algorithms that can learn from data.

The algorithm to be built should be able to make decisions based on historical patterns or events, and when given new input, it can predict what would happen next.

In other words, machine learning is a kind of artificial intelligence that enables software programs to be more precise in predicting outcomes without explicit programming.

After the Algorithms are trained in data science methods, they can independently make decisions and predictions.

As we feed new, larger datasets into them, these AIs learn how to best perform their tasks and become more adept at solving problems.

The following are the top 7 machine learning algorithms based on popularity and real-world usage.

1. Artificial Neural Networks (ANN)

ANNs are one of the most popular forms of Machine learning Algorithms, and they are often used in conjunction with other ML algorithms. The first studies on AI date back to 1943, when Warren McCulloch and Walter Pitts developed a model for neural networks based on mathematics.
Researchers found that ANNs could solve complex pattern recognition and learning problems.

Applications of Artificial Neural Networks:

Pattern Recognition, Image Analysis, Classifying data, Creating Charts/Graphs from Data Sets, etc.
Stochastic Gradient Descent (SGD) is the application of gradient descent for optimisation problems based on stochastic methods. SGD is one of the most well-known algorithms in this category, and it has been used in a wide range of other applications. It’s one of the most efficient approaches to minimise quadratic loss functions.

Stochastic means based on a probability distribution and gradient descent is an iterative optimisation algorithm that can be used to find the local minimum of a function.

Advantages of Artificial Neural Networks:

Based on a mathematical model of how the neural connections between neurons in the brain work
They are resilient to damage and have the ability to self-repair.

2. Naïve Bayes Classifier Algorithm

It is a simple algorithm based on Bayes’ theorem. In the words of Jeff Han, the company’s chief executive, “Computers are good at calculating Probability.

People are good at common sense.

We can represent this model with the following equation:

Where, P(A) = Probability that event A occurs P(B) = Probability that event B occurs P(A|B) = Probability of A given B P(B|A) = Probability of B Given A

Applications of Naïve Bayes Classifier:

Classifying Emails, Spam Filtering, Search engines, etc.

The Naïve Bayes Classifier is used for low-resource, high throughput problems. The most popular examples are RSS feeds and email spam filters.

Advantages of Naïve Bayes Classifier Algorithm:

Real-time classification, scalable, highly accurate.

3. Support Vector Machine Learning Algorithm (SVM)

Support Vector Machines (SVMs) are subgroups in a family of supervised learning techniques used for classification and regression analysis.

Support vector machines are related to the principle of linear separation, but they are much more powerful when it comes to non-linear separations.

The SVM is an example of one class of algorithms used when there is a large class imbalance. This is when the number of instances in one class is significantly higher than the other.

Applications of SVM:

Classifying Data, Sequence Recognition, Stock Market Analysis & Prediction, Natural Language Processing.

Advantages of SVM:

It can be used to solve large and complex classification problems.

4. K-Means Clustering Algorithm

Clustering is the process of putting a group of objects together so that they have things in common (known as a cluster) and distinct things.

The K-Means Clustering Algorithm is an unsupervised learning procedure that aims to partition n items into k clusters, with each observation assigned to the cluster with the nearest mean, becoming a prototype for the entire cluster.

K-Means is an iterative approach in which we start with an arbitrary initial condition and improve it until a stopping criterion is satisfied. Because the number of clusters k has already been determined, as well as the number of observations n that must be grouped, we may proceed.

Applications of K-Means Clustering:

Recommendation systems, Customer segmentation, image processing & pattern recognition.

Advantages of K-Means Clustering:

It is fast, simple, and easy to implement.

5. K-Nearest Neighbors Algorithm

The K-nearest neighbors (KNN) algorithm is a non-parametric method used for classification and regression analysis. KNN can be viewed as an inductive bias in locally weighted linear regression direction, inspired by the k-nearest neighbor rule.

Applications of K-Nearest Neighbors:

Classifying Data, Pattern Recognition, Stock Market Analysis & Prediction, Natural Language Processing.

Advantages of K-Nearest Neighbors:

KNN makes few assumptions about the underlying data distribution, and so it can be used in cases where parametric approaches fail or are inappropriate. Furthermore, KNN classifiers can be built extremely quickly compared to other models and can function well when working with enormous data sets.

K-Means and KNN both belong to the class of unsupervised learning methods used to find hidden patterns in unlabeled data. They can be viewed as stepping stones towards more sophisticated supervised learning algorithms like artificial neural networks (ANNs).

6. Decision Tree Machine Learning Algorithm

A decision tree is a way to represent the decision process involved in making a classification visually.

Applications of Decision Tree Machine Learning Algorithm:

Classifying Data, Sequence Recognition, Stock market analysis & prediction, Customer Segmentation.

Advantages of Decision Trees:

Decision trees effectively capture non-linear relationships in the data and are also suitable for sparse datasets. Decision trees can be built very rapidly once they have been created.

Decision Trees are one of the most intuitive machine learning algorithms due to their top-down and bottom-up approach with decision nodes at every branch point – these decisions allow us to build an accurate model that makes good predictions.

The decision tree is made by repeatedly splitting the set of all training examples on their feature values until each subset represents a single observation. A leaf node is created at every split point, which defines the class for that observation.

7. Linear Regression Machine Learning Algorithm

Linear regression is a frequently used statistical procedure for analysing the connection between a scalar dependent variable y and independent variables x.

Each xi (1 ≤ i ≤ n) is associated with an m-dimensional vector of real numbers (xi, 1 ≤ i ≤ m), the ith component of which is referred to as the regression coefficient.

The Yi’s are continuous dependent variables, and Xj’s (1 ≤ j ≤ n) R’x are called independent variables.

Application of Linear Regression Machine Learning Algorithm:

Predicting Sales, Temperature Prediction, Predicting Stock Market Prices, etc.

Advantages of Linear Regression:

Linear regression is a machine learning technique requiring a minimal understanding of matrix algebra. It is a powerful technique that can be applied to a wide range of problems, particularly those with linear relationships between the dependent and independent variables.

The basic assumption underlying linear regression is that a linear model generates pairs. By estimating the parameters of the linear model, we can make an educated guess about the expected value of y based on x.


If you have read through this blog, you will hopefully have a better understanding of the different machine learning algorithms.

If there is any other topic related to Machine Learning that you want us to cover, please mention it in the comment section.