Top 10 Machine Learning Algorithms You Must Learn In 2024

January 17th, 2024
machine learning algorithms

In the recent times, the world has already witnessed a whole lot of transformation in the tech sector. Computing has gone from large mainframes to PCs and is currently on the cloud. But you think this is the peak of this transformation, then you are wrong. All this is just the beginning and more technological advancements are yet to happen. Whether your goal is to become a data scientist or step into a similar role, you will need to learn these top 10 machine learning algorithms. For 2024, here is the list that will help you!

Top 10 Machine Learning Algorithms for 2024

Here is a list of the top 10 machine learning algorithms for 2024. We will discuss each in detail later in this blog.

  1. Linear Regression
  2. Decision Tree
  3. Naive Bayes Algorithm
  4. K-means
  5. Dimensionality Reduction Algorithms
  6. Logistic Regression
  7. SVM Algorithm
  8. KNN Algorithm
  9. Random Forest Algorithm
  10. Gradient Boosting Algorithm & Ada Boosting Algorithm
Machine Learning Algorithms

Here is a list of popular machine learning algorithms in detail to help you better understand why each of these is important.

1. Linear Regression

Linear regression helps in estimating the real values of number of calls, total sales, etc. on the basis of continuous variable(s). The relationship between dependent and independent variables is established by fitting the apt line, which is represented by the following linear equation-

Y=a*X + b


Y = dependent variable

a = slope

X = independent variable

b = intercept

To derive the coefficients a and b, the sum of the squared distance difference between the regression line and data points is minimized.

2. Decision Tree

Decision Tree is one of the most popular ones on our list of top 10 machine learning algorithms in use today. It classifies problems that work aptly on continuous as well as categorical dependent variables.

3. Naive Bayes Algorithm

The Naive Bayes Algorithm makes the assumption that there is no relation between two features within a class. When it comes to calculating the supposed probability of a certain outcome, it considers independent properties of each feature.

Learn more about ML from this igmGuru's Machine Learning training to start your career as a Data Scientist, Research Scientist, or Machine Learning Engineer.

4. K-means

K-means is an unsupervised learning algorithm used for solving clustering issues. All datasets are classified in specific amounts of clusters so that all data points contained within a cluster are either heterogeneous or homogeneous from the data contained in other clusters.

5. Dimensionality Reduction Algorithms

Research organizations, corporates, and government agencies are storing and analyzing humongous amounts of data. Raw data is like a mine of gold that contains valuable information in forms of patterns and trends. This algorithm plays a huge role in helping to find relevant information.

6. Logistic Regression

With Logistic Regression, discrete values (generally binary values such as 0/1) are estimated from a group of independent variables. It is also widely referred to as logit regression because it aids in predicting the probability of a situation by fitting data into a logit function.

To improve logistic regression models, these methods are often used-

  • Eliminating features
  • Using non-linear models
  • Including interaction terms
  • Regularizing techniques

7. SVM Algorithm

SVM or the Support Vector Machine Algorithm which allows plotting raw data as points within an n-dimensional space. It is a method of classification algorithms and consists of lines called classifiers.

8. KNN Algorithm

KNN or K-Nearest Neighbors Algorithm is applicable for both regression and classification problems. This simple algorithm is used for storing all cases that are available and takes the k neighbors majority vote to classify any new cases.

9. Random Forest Algorithm

Random Forest Algorithm refers to a collection of decision trees which is used for classifying new objects on the basis of its attributes. Every tree is classified wherein the tree ‘votes' for the class. The forest then picks the one having the most number of votes.

10. Gradient Boosting Algorithm & AdaBoosting Algorithm

The AdaBoosting and Gradient Boosting Algorithms are highly useful boosting algorithms employed for handling gigantic amounts of data. It is used for making highly accurate predictions.

Also Read- 20 Most Important Machine Learning Interview Questions

Types of Machine Learning Algorithms 

There are 4 main types of machine learning algorithms that you should know about.

  1. Supervised Learning

In supervised learning, algorithms learn from labeled data. In simple words, it receives input data along with corresponding right output labels during the process. The purpose is to train it to make predictions around accurate labels for unseen and new data. Instances of supervised learning algorithms are-

  • Random Forests
  • Decision Trees
  • Naive Bayes
  • Support Vector Machines
  1. Unsupervised Learning

In unsupervised learning, algorithms take into consideration unlabeled data that do not have any predefined output labels. The goal is to uncover relationships, structures, and patterns within the data. Common techniques for unsupervised learning are-

  • Hierarchical clustering
  • K-means
  • Dimensionality Reduction Methods (such as t-SNE & PCA)
  1. Semi-supervised Learning

The semi-supervised learning approach is a combination of labeled and unlabeled data. It takes into consideration the limited amount of labeled data along with the gigantic set of unlabeled data for improving the learning process. In this process, the unlabeled data is effectively used to overcome the limitations attached with relying only on limited labeled data.

  1. Reinforcement Learning

Reinforcement learning is special in the sense that it is inspired by the way humans learn from trial and error. In this, an agent mingles in an environment where it learns to take decisions for maximizing cumulative rewards. Based on its actions, the agent will receive feedback via penalties or rewards. Consequently, the agent starts to take actions on those that will lead to favorable outcomes.

It is widely used in game playing, autonomous systems, and robotics.


We hope this list of the top 10 machine learning algorithms has helped in better understanding what you will be working on once you step into this world. A career in machine learning will yield fruitful results for those who are keen on being a part of a growing sector. This ever-changing field requires you to be on your toes and keep learning to keep yourself updated. Learn Machine Learning to build a better career growth for a long run.

Machine Learning Algorithms FAQs (Frequently Asked Questions)

Q 1. What are 2 main types of machine learning algorithms?

Answer. The two main types of ML algorithm are - supervised and unsupervised learning.

Q 2. Is ML a form of AI?

Ans. ML or machine learning is one of the applications of AI or artificial intelligence. It is used for extracting information from data and beginning the cycle of autonomous learning.

Q 3. What is CNN in machine learning?

Ans. CNN in machine learning refers to convolutional neural networks. It is a subset of ML.

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