Machine Learning Algorithms

Machine Learning algorithms are the programs that can learn the hidden patterns from the data, predict the output, and improve the performance from experiences on their own. Different algorithms can be used in machine learning for different tasks, such as simple linear regression that can be used for prediction problems like stock market prediction, and the KNN algorithm can be used for classification problems.

In this topic, we will see the overview of some popular and most commonly used machine learning algorithms along with their use cases and categories.

Types of Machine Learning Algorithms

Machine Learning Algorithm can be broadly classified into three types:

  1. Supervised Learning Algorithms
  2. Unsupervised Learning Algorithms
  3. Reinforcement Learning algorithm

The below diagram illustrates the different ML algorithm, along with the categories:

Machine Learning Algorithms

1) Supervised Learning Algorithm

Supervised learning is a type of Machine learning in which the machine needs external supervision to learn. The supervised learning models are trained using the labeled dataset. Once the training and processing are done, the model is tested by providing a sample test data to check whether it predicts the correct output.

The goal of supervised learning is to map input data with the output data. Supervised learning is based on supervision, and it is the same as when a student learns things in the teacher’s supervision. The example of supervised learning is spam filtering.

Supervised learning can be divided further into two categories of problem:

  • Classification
  • Regression

Examples of some popular supervised learning algorithms are Simple Linear regression, Decision Tree, Logistic Regression, KNN algorithm, etc. 

2) Unsupervised Learning Algorithm

It is a type of machine learning in which the machine does not need any external supervision to learn from the data, hence called unsupervised learning. The unsupervised models can be trained using the unlabelled dataset that is not classified, nor categorized, and the algorithm needs to act on that data without any supervision. In unsupervised learning, the model doesn’t have a predefined output, and it tries to find useful insights from the huge amount of data. These are used to solve the Association and Clustering problems. Hence further, it can be classified into two types:

  • Clustering
  • Association

Examples of some Unsupervised learning algorithms are K-means Clustering, Apriori Algorithm, Eclat, etc. 

3) Reinforcement Learning

In Reinforcement learning, an agent interacts with its environment by producing actions, and learn with the help of feedback. The feedback is given to the agent in the form of rewards, such as for each good action, he gets a positive reward, and for each bad action, he gets a negative reward. There is no supervision provided to the agent. Q-Learning algorithm is used in reinforcement learning. 

List of Popular Machine Learning Algorithm

  1. Linear Regression Algorithm
  2. Logistic Regression Algorithm
  3. Decision Tree
  4. SVM
  5. Naïve Bayes
  6. KNN
  7. K-Means Clustering
  8. Random Forest
  9. Apriori
  10. PCA

1. Linear Regression

Linear regression is one of the most popular and simple machine learning algorithms that is used for predictive analysis. Here, predictive analysis defines prediction of something, and linear regression makes predictions for continuous numbers such as salary, age, etc.

It shows the linear relationship between the dependent and independent variables, and shows how the dependent variable(y) changes according to the independent variable (x).

It tries to best fit a line between the dependent and independent variables, and this best fit line is knowns as the regression line.

The equation for the regression line is:

y= a0+ a*x+ b

Here, y= dependent variable

x= independent variable

a= Intercept of line.

Linear regression is further divided into two types:

  • Simple Linear Regression: In simple linear regression, a single independent variable is used to predict the value of the dependent variable.
  • Multiple Linear Regression: In multiple linear regression, more than one independent variables are used to predict the value of the dependent variable.

2. Logistic Regression

Logistic regression is the supervised learning algorithm, which is used to predict the categorical variables or discrete values. It can be used for the classification problems in machine learning, and the output of the logistic regression algorithm can be either Yes or NO, 0 or 1, Red or Blue, etc.

Logistic regression is similar to the linear regression except how they are used, such as Linear regression is used to solve the regression problem and predict continuous values, whereas Logistic regression is used to solve the Classification problem and used to predict the discrete values.

Instead of fitting the best fit line, it forms an S-shaped curve that lies between 0 and 1. The S-shaped curve is also known as a logistic function that uses the concept of the threshold. Any value above the threshold will tend to 1, and below the threshold will tend to 0.

3. Decision Tree Algorithm

A decision tree is a supervised learning algorithm that is mainly used to solve the classification problems but can also be used for solving the regression problems. It can work with both categorical variables and continuous variables. It shows a tree-like structure that includes nodes and branches, and starts with the root node that expand on further branches till the leaf node. The internal node is used to represent the features of the dataset, branches show the decision rules, and leaf nodes represent the outcome of the problem.

Some real-world applications of decision tree algorithms are identification between cancerous and non-cancerous cells, suggestions to customers to buy a car, etc.

4. Support Vector Machine Algorithm

A support vector machine or SVM is a supervised learning algorithm that can also be used for classification and regression problems. However, it is primarily used for classification problems. The goal of SVM is to create a hyperplane or decision boundary that can segregate datasets into different classes.

The data points that help to define the hyperplane are known as support vectors, and hence it is named as support vector machine algorithm.

Some real-life applications of SVM are face detection, image classification, Drug discovery, etc. Consider the below diagram:

Machine Learning Algorithms

As we can see in the above diagram, the hyperplane has classified datasets into two different classes.

5. Naïve Bayes Algorithm:

Naïve Bayes classifier is a supervised learning algorithm, which is used to make predictions based on the probability of the object. The algorithm named as Naïve Bayes as it is based on Bayes theorem, and follows the naïve assumption that says’ variables are independent of each other.

The Bayes theorem is based on the conditional probability; it means the likelihood that event(A) will happen, when it is given that event(B) has already happened. The equation for Bayes theorem is given as:

Machine Learning Algorithms

Naïve Bayes classifier is one of the best classifiers that provide a good result for a given problem. It is easy to build a naïve bayesian model, and well suited for the huge amount of dataset. It is mostly used for text classification

6. K-Nearest Neighbour (KNN)

K-Nearest Neighbour is a supervised learning algorithm that can be used for both classification and regression problems. This algorithm works by assuming the similarities between the new data point and available data points. Based on these similarities, the new data points are put in the most similar categories. It is also known as the lazy learner algorithm as it stores all the available datasets and classifies each new case with the help of K-neighbours. The new case is assigned to the nearest class with most similarities, and any distance function measures the distance between the data points. The distance function can be Euclidean, Minkowski, Manhattan, or Hamming distance, based on the requirement. 

7. K-Means Clustering

K-means clustering is one of the simplest unsupervised learning algorithms, which is used to solve the clustering problems. The datasets are grouped into K different clusters based on similarities and dissimilarities, it means, datasets with most of the commonalties remain in one cluster which has very less or no commonalities between other clusters. In K-means, K-refers to the number of clusters, and means refer to the averaging the dataset in order to find the centroid.

It is a centroid-based algorithm, and each cluster is associated with a centroid. This algorithm aims to reduce the distance between the data points and their centroids within a cluster.

This algorithm starts with a group of randomly selected centroids that form the clusters at starting and then perform the iterative process to optimize these centroids’ positions.

It can be used for spam detection and filtering, identification of fake news, etc.

8. Random Forest Algorithm

Random forest is the supervised learning algorithm that can be used for both classification and regression problems in machine learning. It is an ensemble learning technique that provides the predictions by combining the multiple classifiers and improve the performance of the model.

It contains multiple decision trees for subsets of the given dataset, and find the average to improve the predictive accuracy of the model. A random-forest should contain 64-128 trees. The greater number of trees leads to higher accuracy of the algorithm.

To classify a new dataset or object, each tree gives the classification result and based on the majority votes, the algorithm predicts the final output.

Random forest is a fast algorithm, and can efficiently deal with the missing & incorrect data. 

9. Apriori Algorithm

Apriori algorithm is the unsupervised learning algorithm that is used to solve the association problems. It uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected to each other. This algorithm uses a breadth-first search and Hash Tree to calculate the itemset efficiently.

The algorithm process iteratively for finding the frequent itemsets from the large dataset.

The apriori algorithm was given by the R. Agrawal and Srikant in the year 1994. It is mainly used for market basket analysis and helps to understand the products that can be bought together. It can also be used in the healthcare field to find drug reactions in patients. 

10. Principle Component Analysis

Principle Component Analysis (PCA) is an unsupervised learning technique, which is used for dimensionality reduction. It helps in reducing the dimensionality of the dataset that contains many features correlated with each other. It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation. It is one of the popular tools that is used for exploratory data analysis and predictive modeling.

PCA works by considering the variance of each attribute because the high variance shows the good split between the classes, and hence it reduces the dimensionality.

Some real-world applications of PCA are image processing, movie recommendation system, optimizing the power allocation in various communication channels.

Top 10 Machine Learning Algorithms For Beginners: Supervised, and More

Table of Contents

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How Learning These Vital Algorithms Can Enhance Your Skills in Machine Learning

List of Popular Machine Learning Algorithms


In a world where nearly all manual tasks are being automated, the definition of manual is changing. There are now many different types of Machine Learning algorithms, some of which can help computers play chess, perform surgeries, and get smarter and more personal.

We are living in an era of constant technological progress, and looking at how computing has advanced over the years, we can predict what’s to come in the days ahead. 

One of the main features of this revolution that stands out is how computing tools and techniques have been democratized. Data scientists have built sophisticated data-crunching machines in the last 5 years by seamlessly executing advanced techniques. The results have been astounding. 

The many different types of machine learning algorithms have been designed in such dynamic times to help solve real-world complex problems. The ml algorithms are automated and self-modifying to continue improving over time. Before we delve into the top 10 machine learning algorithms you should know, let’s take a look at the different types of machine learning algorithms and how they are classified. 

Machine learning algorithms are classified into 4 types:

  • Supervised
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

Read More: Supervised and Unsupervised Learning in Machine Learning

However, these four types of ml algorithms are further classified into more types. 

Below is the list of Top 10 commonly used Machine Learning (ML) Algorithms:

  • Linear regression
  • Logistic regression
  • Decision tree
  • SVM algorithm
  • Naive Bayes algorithm
  • KNN algorithm
  • K-means
  • Random forest algorithm
  • Dimensionality reduction algorithms
  • Gradient boosting algorithm and AdaBoosting algorithm

Read More: How to Become a Machine Learning Engineer?

How Learning These Vital Algorithms Can Enhance Your Skills in Machine Learning

If you’re a data scientist or a machine learning enthusiast, you can use these techniques to create functional Machine Learning projects.

There are three types of most popular Machine Learning algorithms, i.e – supervised learning, unsupervised learning, and reinforcement learning. All three techniques are used in this list of 10 common Machine Learning Algorithms: 

Machine Learning Algorithms

Also Read: Training for a Career in AI & Machine Learning

1. Linear Regression

To understand the working functionality of Linear Regression, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arranging them using a combination of these visible parameters. This is what linear regression in machine learning is like.

In this process, a relationship is established between independent and dependent variables by fitting them to a line. This line is known as the regression line and is represented by a linear equation Y= a *X + b.

In this equation:

  • Y – Dependent Variable
  • a – Slope
  • X – Independent variable
  • b – Intercept

The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.

2. Logistic Regression

Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logit regression.

These methods listed below are often used to help improve logistic regression models:

  • include interaction terms
  • eliminate features
  • regularize techniques
  • use a non-linear model

3. Decision Tree

Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems. It works well in classifying both categorical and continuous dependent variables. This algorithm divides the population into two or more homogeneous sets based on the most significant attributes/ independent variables.

4. SVM (Support Vector Machine) Algorithm

SVM algorithm is a method of a classification algorithm in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.

5. Naive Bayes Algorithm

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.

A Naive Bayesian model is easy to build and useful for massive datasets. It’s simple and is known to outperform even highly sophisticated classification methods.

6. KNN (K- Nearest Neighbors) Algorithm

This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it’s more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.

KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!

Things to consider before selecting K Nearest Neighbours Algorithm: 

  • KNN is computationally expensive
  • Variables should be normalized, or else higher range variables can bias the algorithm
  • Data still needs to be pre-processed.

7. K-Means

It is an unsupervised learning algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let’s call that number K) in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.

How K-means forms clusters:

  • The K-means algorithm picks k number of points, called centroids, for each cluster.
  • Each data point forms a cluster with the closest centroids, i.e., K clusters.
  • It now creates new centroids based on the existing cluster members.
  • With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.

8. Random Forest Algorithm

A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:

  • If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
  • If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during this process.
  • Each tree is grown to the most substantial extent possible. There is no pruning. 

9. Dimensionality Reduction Algorithms

In today’s world, vast amounts of data are being stored and analyzed by corporates, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information – the challenge is to identify significant patterns and variables.

Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.

10. Gradient Boosting Algorithm and AdaBoosting Algorithm

Gradient Boosting Algorithm and AdaBoosting Algorithm are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.

In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.


If you want to build a career in machine learning, start right away. The field is increasing, and the sooner you understand the scope of machine learning tools, the sooner you’ll be able to provide solutions to complex work problems. However, if you are experienced in the field and want to boost your career, you can take-up the Post Graduate Program in AI and Machine Learning in partnership with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning algorithm with the Tensor flow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.

Also, prepare yourself for Machine Learning interview questions to land at your dream job!