Top 5 Machine Learning Algorithms You Should Learn in 2024

Decision Tree Algorithm, Support Vector Method Algorithm, Logistic Regression, K-means Clustering Algorithm, and Naïve Bayesian classification, are 5 basic ML algorithms.

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7 min readAug 1, 2021

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Hello guys, if you are a beginner in Machine Learning and Data Science field and wondering where to start then you have come to the right place. Earlier, I have shared the best Machine Learning courses and certifications, and books, and in this article, I am going to share with you 5 essential Machine Learning Algorithms which every Data Scientist should know.

Yes, I am talking about Decision Tree Algorithm, Support Vector Method Algorithm, Logistic Regression, K-means Clustering Algorithm, and Naive Bayesian classification, these are the are 5 basic Machine learning algorithms you will learn in this article.

You may know that Machine Learning and Artificial Intelligence have become more and more important in this increasingly digital world. They are now providing a competitive edge to businesses like Netflix's Movie recommendations.

If you have just started in this field and looking for what to learn then I am going to share 5 essential Machine learning algorithms you can learn as a beginner.

These essential algorithms form the basis of most common Machine learning projects and having a good knowledge of them will not only help you to understand the project and model quickly but also to change them as per your need.

Machine learning by a simple word is the science or the field of making the computer learn like a human by feeding it with the data and without being programmed and it separate into two categories the first one is classification problems which the machine needs to classify between two objects or more like between human and animal and the second is regression problems which the machine need to produce an output based on a previous data.

Machine learning is one of the most used fields in Artificial Intelligence today like face recognition Software's, self-driven cars, voice recognition, forecasting, and also when you are applying filters when you are using Snapchat.

Learning these skills will make you in one of the highest-paid jobs with an average annual salary of $141,205 dollars in the USA inside a business that is worth $3.9 trillion in 20243.

In this article, we will see 5 of the most used machine learning algorithms that make you understand how AI and complicated IT technologies work, and also you can create such an artificial intelligence software with these algorithms and maybe use it in your daily life.

5 Essential Machine Learning Algorithms for to Learn in 2024

While Machine Learning is a vast field and there are many Algorithms, these are some of the essential algorithms which play an important role in many Machine learning projects. Even having a basic knowledge of these Algorithms can help you greatly in working on Machine learning projects and understanding them.

1. Decision Tree Algorithm

The decision tree is a type of classification algorithm which used in supervised learning techniques but it can be used for two main problems the classification and regression problems.

For the classification problems, it is an answer like “True” or “False” where it will determine the answer by a bunch of logical if-then statements such as determining the type of five cars based on some features and for the regression problems is used if you want to get the answer for a numeric problem like determine the price of home-based on some features.

If you want to learn then I suggest you to can further check Decision Trees, Random Forest, AdaBoost, and XGBoost in Python course by Start Tech Academy on Udemy for a solid understanding of the Decision Tree Algorithm. I highly recommend this course.

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2. Support Vector Method Algorithm

Support vector machine is an algorithm for the advanced cases of classification problems like classify between two types of dogs that are similar to each other in color, size, body, and so on.

It separates your data into 2 classes and trying to find the best line that fits your model called hyperplane so the two classes have some space between them and that line and that space is called margin and by doing so it can classify some big classification problems like determining the gender of someone on a picture.

Also, it can be used for regression problems as well. You can further check the Support Vector Machines in Python course on Udemy to learn more about this algorithm in Machine Learning and Artificial Intelligence space.

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3. Logistic Regression

It is an algorithm for regression problems and a way of determining the relationship between two variables and one of them is dependent and the others one is the independent variable and like a predictive model.

The dependent variable is the variable you want to predict and the independent variable is the variable is you give to the algorithm to learn.

The good thing in this algorithm is considered a strong statistical technique for predicting events that include one or more independent variables.

If you want to learn more then you can also check out the Logistic Regression in Python course by Start Tech Academy on Udemy. It’s another great course from Start Tech after their Decision tree one and you can do Predictive Modeling using Python after going through this course.

4. K-means Clustering Algorithm

K-means is a type of clustering algorithm which is a set of algorithms that uses unsupervised learning techniques to learn and solve the problem and only it works with numeric data.

This algorithm can solve classification problems without the need for previous data to learn or train, it see the difference and similarity of a bunch of last say pictures, and try to group them based on those features.

This means that any categorical variable should be converted to a numeric variable before this algorithm can be applied. You can further see Cluster Analysis and Unsupervised Machine Learning in Python course by Lazy Programmer on Udemy to learn about K-means and other clustering algorithms.

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5. Naive Bayesian classification

Naive Bayesian is a classification algorithm that is good for a large dataset and it is the best algorithm that uses statistical calculation in its background like probabilities and so even the advanced ones to solve problems of classification and also it is for imperative analysis.

This algorithm is used in many daily applications such as email spam detection, face recognition, pattern recognition, and is considered as one of the easiest and fastest algorithms to predict the class of test data set.

You can also check out Bayesian Machine Learning in Python: A/B Testing to learn more about the practical usage of Bayesian methods in the real world.

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That’s all about essential machine learning algorithms every Machine learning engineer should learn. We have discussed many machine learning algorithms in this article and they are the most commonly used among machine learning engineers where they can create an app to facilitate their lives or for complex Software.

Of course, there are other algorithms that we didn’t discuss here but they are the main ones and you can start learning them if you are considering a career as a machine learning engineer, but these essential Machine learning algorithms are great to start with.

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Thanks for reading this article so far. If you find these essential Machine learning Algorithms useful then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. — If you are determined on making a career in the Machine Learning field and looking for the best online courses to level up your skills then I highly recommend you check out the Machine Learning A-Z — Hands-On Python course by Kirill Eremenko on Udemy. It’s one of the most popular and comprehensive courses to learn Machine Learning online.

Machine Learning A-Z (Python & R in Data Science Course)

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