Top 10 Machine Learning Algorithms

Machine Learning Algorithms are like virtual brainpower, helping the computer to learn from the data. These algorithms are used, for weather prediction to suggesting which movies you have to watch next on Netflix.
So, in this article, we have listed thetop 10 Machine Learning Algorithms that you must have to know if you are an aspiring data scientist or machine learning enthusiast.

Machine Learning is the branch of Artificial Intelligence that focuses on building models that can automatically learn and perform tasks based on data. A Machine Learning process involves Data Collection, preprocessing, Feature Engineering, Model Selection, Training, Evaluation, Model Deployment, and monitoring and maintenance.

Now the question is, How do Machines do all these?

Here, Algorithms come into the picture. These algorithms help in:

• Pattern Recognition
• Predictive Analysis
• Automating Complex Tasks
• Improving Accuracy
• Customized Solution 
• Feature Selection
• Anomaly Detection

In this article, we will discuss the top 10 Machine Learning Algorithms you must know.

So, let’s get started.

Table of Content

• What are Machine Learning Algorithms
• Types of Machine Learning Algorithms
  • Supervised Machine Learning Algorithm
  • Unsupervised Machine Learning Algorithm
  • Reinforcement Machine Learning Algorithm
• Top 10 Machine Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Naive Bayes Algorithm
  • Support Vector Machine
  • Decision Tree
  • K-Nearest Neighbor
  • Random Forest Algorithm
  • Gradient Boosting Algorithm
  • K-Means Cluster

What are Machine Learning Algorithms?

Machine Learning Algorithm is a set of instructions that allow a computer to learn from data and make predictions or decisions without being explicitly programmed. Machine Learning Algorithms are used in various applications, such as Natural Language Processing, Computer Vision, Natural Language Processing, and Fraud Detection.

Must Read: What is Machine Learning and How does it Work?

Must Read: Basics of Machine Learning

Types of Machine Learning Algorithms

There are three main types of Machine Learning Algorithms:

Supervise Machine Learning Algorithm

A type of Machine Learning where the algorithm is trained on data that has been labeled with the desired output. The algorithms learn to predict the output for new data points.

The algorithm makes a prediction or decision based on input data and is corrected when its prediction is incorrect.

Some of the common Supervised Learnings are:

• Linear Regression
• Support Vector Machine
• K-Nearest Neighbors
• Naive Bayes

Must Read: Supervised Learning in Action: Real-World Applications and Examples

Unsupervised Machine Learning Algorithm

A type of machine learning algorithm where the algorithm is trained on the data that has not been labeled with the desired output. In simple terms, unsupervised algorithms are used when the information used to train is neither classified nor labeled.

This algorithm studies how the system can infer a function to describe a hidden structure from unlabeled data.

Some of the Common Unsupervised Learning Algorithms are:

• Clustering Algorithm (like K-means, Hierarchical Clustering)
• Association ALgorithm (like Apriori, FP-Growth)

Must Read: What is Unsupervised Learning?

Must Read: Difference Between Supervised and Unsupervised Machine Learning Algorithm

Reinforcement Machine Learning Algorithm

A type of Machine learning where agents learn to behave in an environment by trial and error. The agent receives rewards for taking action that leads to desired outcomes and penalties for taking that leads to undesired outcomes.

Over the period, the agent learns to take action that maximizes the rewards it receives.

Application of Reinforcement Learning includes Video Games, Robotics, and Traffic Control.

Some of the common Reinforcement Machine Learning Algorithms are:

• Q-Learning
• SARSA
• Deep Q-Learning
• Monte Carlo Methods

Must Read: Reinforcement Learning-A Complete Guide

Top 10 Machine Learning Algorithms

Linear Regression

A type of supervised machine learning algorithm that is used to predict the continuous numerical value. It is a linear approach to modeling the relationship between a dependent variable and one or more independent variables.
Linear Regression Models a linear relationship with a line of regression or regression line between two or more continuous variables.
Here, the goal is to find the equation of a straight line that will use the dependent variable to predict the value of an independent variable with maximum accuracy or minimum error.

Here, the equation of a line can be found by using the equation of a line:

y = mx + c

where,

y: dependent variable (target variable-variable that we are trying to predict)
x: independent variable
m: slope
c: y-intercept

To know More About Linear Regression, Read our Blog: All About Linear Regression

Logistic Regression

Logistic regression is a statistical method that helps us predict the likelihood of something happening based on certain factors. It’s often used when the outcome can be one of two possibilities: YES or NO, WIN or LOSE, TRUE or FALSE etc.

For example, it can help predict whether an email is a spam (yes) or not spam (no) based on the words used in the message.

It gives us a formula that we can use to get a probability value between 0 and 1, helping us make informed guesses in various situations.

y = e^{(b₀ + b₁*x)} / (1 + e^{(b₀ + b₁*x)})

To know More About Logistic Regression, Read our Blog: : All That You Need to Know About Logistic Regression

Must Read: How to Improve the Accuracy of Logistic Regression

Naive Bayes Algorithm

A type of machine learning algorithm to predict the likelihood of different outcomes based on certain conditions, using principles from probability theory.

In simple words, it’s a “guessing game” algorithm that makes educated guesses on new information based on what it has learned from past data.

For example, it can be used in email filtering systems to guess whether an email is spam or not based on the frequency of certain words appearing in the email.

The “naive” part comes from the assumption that all features (like the words in the email) are independent, which isn’t always the case, but it allows the algorithm to be simple and fast.

To know More About Naive Bayes Algorithm, Read our Blog: Naïve Bayes Algorithm in Machine Learning

Must Read: Introduction to Bayes’ Theorem

Support Vector Machine

A type of machine learning algorithm is used mainly for classification tasks, though it can also be used for regression and other tasks.

Imagine you have a sheet of paper with blue and red dots scattered on it.

SVM helps you find the best possible line (or, in higher dimensions, a plane or a hyperplane) that separates these dots into two groups, blue on one side and red on the other, with the widest possible gap between them.

This “line” is determined based on the data points that are closest to the other group, known as support vectors. It’s a powerful tool in data science for creating robust and accurate predictive models.

To know More About SVM, Read our Blog: : Support Vector Machines with Python Code

Must Read: How Vectors are Used in Machine Learning

Decision Tree

A decision tree algorithm is like a flowchart for making decisions. It splits data into smaller groups based on certain conditions, step by step, until it concludes.

Imagine playing a guessing game where you ask a yes-or-no question at each step to narrow down the options until you can make a guess.

A decision tree works similarly: it asks a series of questions about the data, and based on the answers, it splits the data into smaller groups and asks more questions until it can make a prediction or a decision.

It’s a graphical, easy-to-understand way to analyze complex data and is widely used in machine learning for classification and regression.

To know More About Decision Tree, Read our Blog: : Understanding Decision Tree Algorithm in Machine Learning

K-Nearest Neighbor

It is a machine learning algorithm that works on the principle of similarity.
In simple terms, it predicts the classification or value of a new data point based on the classifications or values of its “Neighbours” in the data set.

Imagine you moved into a new neighbourhood and wanted to estimate the cost of a house. You’d probably look at the prices of the nearby houses to get an idea.

KNN works similarly: given a new data point, it looks at the ‘K’ most similar data points (the ‘Neighbours’) in the dataset. Making a prediction takes a vote (for classification) or an average (for regression). The ‘K’ is a number the user chooses, determining how many neighbours to consider.

It’s a simple yet powerful algorithm, especially useful when the data has well-separated clusters or groups.

To know More About KNN Algorithm, Read our Blog: : KNN Algorithm in Machine Learning

Random Forest Algorithm

The Random Forest algorithm is like assembling a team of experts to help you make a decision, where each expert considers a random subset of factors and gives their opinion.

Here’s how it works in simple terms: It creates multiple decision trees (like the ones we discussed earlier) during the training phase.
Each tree gets a random subset of features and data points to work with, making them “experts” in those subsets.

When it’s time to make a prediction, all the trees in the “forest” get a vote.
The majority vote (for classification) or the average prediction (for regression) is considered as the final output.

This approach helps in getting more accurate and stable predictions as it combines the wisdom of multiple “trees” rather than relying on the decision of a single tree, reducing the likelihood of making a mistake based on the bias or variance of one individual tree.
It’s a popular choice for various machine learning tasks due to its accuracy and ability to work on large datasets.

To know More About Random Forest Algorithm, Read our Blog: : Random Forest Algorithms

Gradient Boosting Algorithm

The gradient boosting algorithm is like a team working together to correct each other’s mistakes until they achieve the best result.

In simpler terms, this algorithm creates a series of decision trees where each tree tries to correct the errors made by the previous ones. Here’s how it works:

• The first tree makes predictions on the data, albeit with some errors.
• The second tree comes in and focuses specifically on the places where the first tree went wrong, trying to correct those errors.
• The third tree then works on the mistakes that are still present, and so on.

This process continues, with each new tree in the series focusing on reducing the errors from the previous trees, until we have a collection of trees that, together, make very accurate predictions. 

It’s a powerful technique often used in machine learning competitions to create high-performing models. It works step-by-step, slowly improving towards the most accurate result.

K-Means Cluster

The K-means clustering algorithm is like organizing a big party where you want to group people into different sections based on their preferences, but you have a limited number of sections available.

Here’s how it works in a simple way:

• You decide on a number of groups (clusters) you want to form, which is “K”.
• You randomly select K spots (centroids) as the initial centers of these groups.
• Each person (data point) then goes to the closest spot, forming K groups.
• You then find a new center (mean) for each group based on the preferences of all the people in the group.
• You repeat steps 3 and 4 until the centers don’t change much between iterations, indicating the groups are relatively stable.

Now, you have K groups of people with similar preferences, making it easier to cater to their likes and dislikes during the party. 

In data science, it’s a popular method for unsupervised learning to find natural groupings within data.