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Top 10 Machine Learning Algorithms 2021

Top 10 Machine Learning Algorithms 2021

Looking for the top 10 machine learning algorithms 2021? 

We have the answers for you.

According to Forbes, popular machine learning algorithms may soon replace a wealth of jobs in fields like manufacturing, transportation (hello, self-driving cars), architecture, healthcare, and even computer science.

Besides the huge advantages that machine learning AI development can bring to the world, there are huge sums of money to be made too. Here are a few amazing case studies of companies who hired DevTeam.Space to build their machine learning products:

  1. Neural Network Library – Machine Learning Application
  2. Hit Factor – Machine Learning Image Recognition App 
  3. High-Speed Vehicle Recognition – Machine Learning Image Recognition Application

What Is Machine Learning (ML)

Let’s cover a few basics first, and let’s start with understanding what ML is. It’s a capability within the overarching umbrella of “Artificial Intelligence” (AI). Check out some pros and cons of this advanced technology in this blog post.

Organizations use ML data science to create computer systems that can “learn” without explicit programming for “learning”. How do these computer systems learn then? Well, they learn to identify data points from very large pools of data.

ML or AI solutions utilize computer algorithms that study and analyze this training data, moreover, these algorithms can observe patterns. E.g., ML algorithms can identify examples, experiences, instructions, etc. in the data sets.

The basic design of AI and ML means that you can use it for pretty much anything. Depending on the training set, the input data, the AI predictor or system can learn just about anything. This AI tutorial explains this in-depth.

After being trained by data scientists, these algorithms can then go on to make decisions and predictions. As we feed larger data sets to them, they learn to perform their tasks better and better thereby boosting performance. You can read our guide “Machine Learning in future software development” for more deep learning insights.

Machine Learning use cases

Machine Learning has a wide range of use in many areas. A few examples are as follows:

  • Applications such as voice assistants in smartphones;
  • Dynamic pricing in the travel industry;
  • Email filtering;
  • Social media recommendations;
  • Personalized marketing;
  • Customer support chatbots;
  • Cybersecurity;
  • Fraud detection in banking and financial services institutions;

Read more examples of ML use cases in “Popular Machine Learning applications and use cases in our daily life”.

It’s no wonder then that the market is ML is growing rapidly. A Cision PR Newswire report projects that the global market for ML will reach $96.7 billion in 2025. This report estimates that this market will grow at an impressive CAGR of 43.8% during the 2019-2025 period.

Every year, more and more companies like Google’s Kaggle are being created with the aim of tapping into this highly lucrative market.

Machine Learning Algorithms explained:


Machine Learning algorithms are used in a variety of applications including:

  • Spam filtering
  • Image tagging
  • Self-driving cars
  • Optical Character Recognition
  • Predictions
  • Anomaly detection
  • Association rules and more

Do ML algorithms come in one flavor only? They don’t! Let’s review the various kinds of ML algorithms, which are as follows:

Supervised learning algorithms: These algorithms use known sets of input and out data, i.e., the data is “labeled”. Such algorithms use this labeled data to train computer systems to answer questions.

Unsupervised learning algorithms: These algorithms use “unlabeled data”, i.e., the data sets don’t contain the answers to questions. Computer systems learn to identify hidden patterns and structures in the data sets.

Semi-supervised learning algorithms: These algorithms use both “labeled” and “unlabeled” data sets. In effect, this kind of ML uses both supervised and unsupervised learning algorithms.

This requires some form of dimensionality reduction in order to turn the data into an interpretable form. For variable reduction, which is used for such things as image recognition, Principal component Analysis or PCA is used.

Reinforced learning algorithms: ML using these algorithms involve a trial-and-error approach. These algorithms “train” computer systems based on feedback, and computer systems “learn” better over time from the “experience”.

Read more about this in our guide “How to build a machine learning filing system to classify books”.

Machine Learning algorithms examples

Some machine learning algorithms or learning models are more popular than others. The following are the top 10 machine learning algorithms examples based on popularity and real-world usage.

Artificial Neural Networks


Artificial Neural Networks are named so because they’re based on the structure and functions of real biological neural networks. Information flows through the network and in response, the neural network changes based on the input and output.  This machine-learning algorithm is used in a number of ways:

  • Character recognition (understanding human handwriting and converting it to text)
  • Image compression
  • Stock market prediction
  • Loan applications

One of the most common uses for Artificial Neural Networks is speech recognition. If you’ve ever used Siri, you’ve probably used an ANN. These types of machine learning algorithms get better with more information – they’re constantly growing. Let’s be real: speech recognition has grown leaps and bounds in accuracy over the last five years.

Naïve Bayes Classifier Algorithm

The Naïve Bayes Classifier Algorithm is a classification machine learning algorithm that works off of the popular Bayes Theorem of Probability. It’s one of the most popular learning algorithms that groups similarities, and is usually used in the following ways:

  • Disease prediction
  • Document classification

The Naïve Bayes Classifier may sound unfamiliar, but you’ve probably encountered it before. The most popular examples are your email spam filter and RSS feeds that filter news into specific categories (Politics, Entertainment, Sports, etc.). 

This algorithm is particularly useful if you have a moderate or large dataset, if the new data has several attributes that can help classify it and if the attributes that describe a certain classification are conditionally independent.

Support Vector Machine Learning Algorithm

A graph of support vector machine learning algorithm

Support Vector Machine is one of the many examples of machine learning algorithms catered to classification. This is used for either classification or regression in instances where the set of data teaches the algorithm about specific classes so it can classify newly added data. SVM is constantly growing and evolving.

SVM is commonly used in:

  • Stock Market forecasting
  • Risk assessment

Most commonly, SVM is used to compare the performance of a stock with other stocks in the same sector. This helps companies make decisions about where they want to invest.

K-Means Clustering Algorithm

A screenshot of Google autocomplete

The K-Means Clustering Algorithm is one of the most popular machine learning examples. It is commonly used in the following applications:

  • Search engines like Yahoo and Bing (to identify relevant results)
  • Data libraries
  • Google image search
  • Microsoft Machine Learning Studio

K-Means Clustering is a simple machine learning algorithm used for clustering, meaning it helps group together similar data sets. This could be anything from images and videos to text documents and web pages. For example, you’re searching Wikipedia for the word Apple.

This could bring up results for both Apple, the technology company, and apple, the fruit. K-Means Clustering would group together results about the technology company apart from results about the fruit, so you can get meaningful results on the actual topic you want to read about.

K-Nearest Neighbors Algorithm

Like K-Means Clustering, K-Nearest Neighbors is another classification and regression machine learning algorithm. It’s most commonly used in:

  • Pattern recognition (like to predict how cancer may spread)
  • Statistical estimation (like to predict if someone may default on a loan)

K-Nearest Neighbors makes predictions by searching through the whole dataset to find the most similar instances (the neighbors) and summarizing the output variable for those instances. Objects are classified by majority votes and assigned the class most common to its neighbors.

Decision Tree Machine Learning Algorithm

An illustration of the Decision Tree

Decision Trees are graphical representations that show all possible outcomes of a decision based on certain conditions. It’s typically used for two things – to classify or to predict – and remains one of the best machine learning algorithms for classification. It has been used in the automate the following functionalities:

  • To help banks classify loan applicants and their probability of defaulting payments
  • To help Gerber Products decide whether or not to use PVC in their baby products
  • Identify at-risk patients and disease trends with Guardian, a tool developed by Rush University Medical Centre

The decision tree algorithm falls into either Classification Trees or Regression Trees. Classification trees are the default and used to split data into different classes based on the response variable.  Regression Trees are used when the target variable is continuous or numerical. This is typically used in a predictive nature.  Because of the nature of the algorithm, if your data has errors, so will your decision tree. These classification problems mena it’s best suited to extensive, meticulously correct data.

Apriori Machine Learning Algorithm

Apriori algorithm is a data-mining machine learning algorithm that generates association rules for a given set of data. It has been used in everything from a college elective system that helps students choose classes to a database that discovers the social status of diabetic people. Its most popular applications include:

  • Google auto-complete
  • Amazon and AWS shopping recommendations
  • Detecting adverse drug reactions
  • Natural language processing for chatbots

Apriori works by using association rules from a given data set. These rules imply that if A occurs, B also occurs. For example, Wal-Mart actually used the Apriori algorithm to increase sales of beer. Wal-Mart studied their big data pools to find that American males who bought diapers on Friday afternoons also frequently bought beer. They moved the beer next to the diapers, and sales increased.

Apriori is beneficial in more than a couple of ways. It also happens to be one of the easiest machine learning algorithms to implement.

Linear Regression Machine Learning Algorithm

Linear Regression is one of the most interpretable machine learning algorithms. It’s easy to explain to others and requires minimal tuning. This is perhaps why it’s one of the most popular algorithms. It can be used to:

  • Estimate Sales
  • Assess Risk

Linear regression works by showing a relationship between two variables and how the change of one variable affects the other. This is why it’s so great in risk assessment and business.

For example, health insurance brokers often use this algorithm to allow for automation of analysis of the number of claims per customer against their age, etc.  If insurance companies find that older customers tended to make more claims, they increase rates for older customers. If they found that older customers didn’t have more accidents, they could lower the rates.

Random Forest Machine Learning Algorithm

Random Forests or Random Decision Forests are a machine learning method of classification and regression. You’ve probably seen them used in the following ways:

  • To help banks predict high-risk loan applicants
  • To predict failure or breakdown of a mechanical part
  • To predict if a patient is likely to develop a chronic disease
  • To predict the average number of social media shares on a post

Its versatility is what gives this algorithm its popularity. Instead of using a single decision tree, Random Forest uses a multitude of decision trees to come up with a solid classification or prediction.

This ensures a more accurate classification because each decision tree is given slightly different data. These variables are very effective because they help preserve accuracy when data is missing. It’s also fairly resistant to outliers (majority always rules) and easily implemented in a couple of lines of code.

Logistic Regression

No, Logistic Regression isn’t for regression problems. It’s actually for classification tasks. The algorithm applies a logistic function to a combination of features that predicts the outcome of a dependent variable. Of course, it wouldn‘t be true to the name if the independent variable wasn’t based on already predicted input variables. It’s split up into three categories:

  • Binary Logistic Regression
  • Multi-nominal Logistic Regression
  • Ordinal Logistic Regression

Binary Logistic Regression is most commonly used when there are two possible binary classifictions or outcomes (yes or no; pass or fail). This can help in ways such as predicting if a student is likely to pass or fail a course or predicting if a tumor is cancerous or not. Multi-nominal Logistic Regression has three or more outcomes with no order, and Ordinal Logistic Regression has three or more outcomes with a natural ordering.

Developing a Machine Learning algorithm: What skills do you in your team?

Now that you have clarity about the popular ML algorithms, you are likely thinking about how to develop one for your mobile or web application.

The key question that probably comes to your mind is – What skills do I need in my team? What skills should I seek in my developers i.e. Java, JavaScript, Agile, Scrum, API development, experience working with IBM or AWS AI systems, etc?

At the time of writing this guide, Python is the most popular language for ML development. Python is simple, and that contributes greatly to its popularity.

Compared to several other languages, you need a shorter time to develop ML code in Python. This popular language has many libraries that make programming easier. Take the example of Pybrain, Scikit-learn, and XGBoost, which are libraries for ML coding using Python. Read more about the popularity of Python in “Top 5 best programming languages for Artificial Intelligence field”.

Wondering how to find competent JavaScript and Python developers and project managers? DevTeam.Space has just the expertise you need to hire. We have built a number of ML and AI software products using all kinds of machine learning models including those above as well as AdaBoost, Logistic Regressionor, Knn, to name but a few.


This list of machine learning algorithms is only the very tip of the iceberg. If your startup or business is looking to benefit from the positive impact of implementing artificial intelligence and machine learning into your business, as a reputed software development company, DevTeamSpace has the expert developers who will make it happen. Just post a simple request and we will get back to you to answer any questions you might have.

Further Reading

Here are a few articles that might also interest you:

How To Build An Investment Portfolio App

How to Build an Education App?

How To Build a Messaging App like WhatsApp?

How To Build AI Management Software

Frequently Asked Questions

What is machine learning algorithms?

These are formulas that allow computer programs to identify trends or patterns in data pools and to cross-reference the results with past results to improve future results.

What are regression algorithms in machine learning?

Regression algorithms are part of the Supervised Machine Learning algorithms family. Regression algorithms are used to predict the output values from pools of data.

What is the difference between AI and ML?

AI or artificial intelligence is currently a stage of computer intelligence that has not yet been reached. Current systems are ML or machine learning systems that are able to cope with a narrow set of interpretations. AI will arrive when computers are able to learn new tasks and skills independently of humans.

What is bagging?

Bootstrap aggregating or bagging is a machine learning meta-algorithm that is designed to improve the accuracy of ML algorithms used in statistical classification.

What is binary classification?

Binary classification is the classification of data into two different groups.


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