Machine learning algorithms have been significantly growing. A survey showed that simple machine learning algorithms grew 36.2% between 2023 and 2030. ML algorithms depend on high-level annotated data for training. Therefore, knowing about the top 10 machine learning algorithms is essential to accelerate your growth. 

Machine learning algorithms refer to computational methods that enable systems to learn from the fed annotated data to improve their accuracy and performance without being explicitly programmed gradually. In the world of data science, these machine learning models help analyze input data, identify patterns in those data, and make predictions or decisions based on that fed data. These machine learning algorithms are categorized into four major types. Let’s check out the various types of machine learning algorithms in detail.

What Are The Four Types Of Machine Learning Algorithms?

Infographics show 4 types of machine learning algorithms

Supervised Machine Learning

Supervised learning models involve training on a labeled or annotated dataset, where every input data point is paired with the correct output data. The primary goal of these models is to learn mapping from input to output data, allowing the model to predict outcomes for new data. Supervised learning models are further categorized into two parts: Classification and Regression. 

A few examples to illustrate the supervised learning algorithms are:

  • Decision tree
  • SVM
  • K-nearest neighbor
  • linear regression

Unsupervised Learning

Unsupervised machine learning deals with datasets with no labeled outputs. In the field of machine learning, this algorithm finds intrinsic structures and hidden patterns in the fed data to generate new data. The unsupervised learning model is divided into Clustering and Dimensionality Reduction. 

A few examples of unsupervised learning algorithms are:

  • K-means
  • LDA
  • PCA
  • Mean shift

Semi-Supervised Learning

The semi-supervised machine learning algorithm is a hybrid approach that uses a small amount of labeled and unlabeled data. This method is particularly useful when labeling expensive or time-consuming data.

Reinforcement Learning

The reinforcement learning method deals with software training to get optimal results. The software is trained to make independent decisions. This is the classic example of a trial-and-error method in data annotation.

To train these models well, learn about the power of generative AI models. Generative AI models are built using ML algorithms, so the insights help.

List Of Top 10 Machine Learning Algorithms

Infographic show top 10 machine learning algorithms in 2024

So, here is the most awaited list of algorithms in machine learning:  

Apriori Algorithm 

  • Use Case: The Apriori algorithm in Machine Learning is often used in market basket analysis, association rule mining, and frequent itemset mining to discover patterns in large datasets with dependent variables.
  • Key Feature: Identifies frequent datasets and generates association rules to determine relationships between items.

Decision Trees Algorithm

  • Use Case: The decision trees learning algorithm used for classification and regression problems. These decisions are made based on feature splits.
  • Key Feature: It is simple to interpret and visualize, making it effective for decisions involving binary classification outcomes or complex decision-making processes.

Random Forests Algorithm

  • Use Case: Random Forest is an ensemble learning method used for regression and classification tasks, often in scenarios where overfitting is a concern.
  • Key Feature: An ensemble method that builds multiple decision trees and merges them to improve accuracy and reduce overfitting for a more accurate and stable prediction.

Linear Regression Algorithm 

  • Use Case: Predicts continuous values, such as forecasting sales or predictive house prices.
  • Key Feature: Used for predictive analysis, these note the relationship using a linear approach between a dependent target variable and one or multiple independent variables.

Logistic Regression Algorithm

  • Use Case: Primarily used for binary classification tasks, such as spam detection or predicting whether an event will occur.
  • Key Feature: Predicts the probability of a binary outcome using a logistic function suited for classification rather than regression.

K-Means Clustering Algorithm

  • Use Case: Unsupervised learning tasks such as customer segmentation or pattern recognition.
  • Key Feature: Organizes data into clusters by minimizing variance within each cluster and maximizing the distance between clusters.

K-Nearest Neighbours Algorithm

  • Use Case: KNN machine-learning algorithms help in classification tasks like image recognition and recommendation systems.
  • Key Feature: An instance-based learning algorithm that makes predictions based on the majority class among the k-nearest neighbors.

Naïve Bayes Classifier Algorithm

  • Use Case: Naive Bayes is often used in text classification tasks, like spam filtering or sentiment analysis.
  • Key Feature: Based on Bayes’ Theorem, it assumes independence among features for simple and efficient classification.

Support Vector Machine Algorithm

  • Use Case: Classification and regression, especially when the dataset is high-dimensional or when a clear margin of separation is required.
  • Key Feature: Maximizes the margin between different classes using support vectors and a hyperplane.

Artificial Neural Networks Algorithm

  • Use Case: Complex tasks such as image classification, natural language processing, and deep learning applications.
  • Key Feature: It consists of layers of nodes (neurons) that mimic the human brain and are capable of learning intricate patterns in large datasets through backpropagation.

List of Popular ML Algorithms In Data Science

Explore the top and popular ML algorithms. These are widely used and soon will be used by various machine learning algorithms. 

AdaBoost

  • Use Case: It is often used for binary classification tasks but can also be applied to multi-class classification and regression.
  • Boosting Technique – AdaBoost combines multiple weak learners (usually decision stumps or shallow trees) into a single strong classifier. It sequentially trains weak classifiers, giving more weight to misclassified instances.
  • Adaptive Weighting – Each iteration adjusts the weights of incorrectly classified examples, focusing the next model on the harder-to-classify cases.

Dimensionality Reduction

  • Use Case: It is applied in high-dimensional datasets to reduce the number of features, making models more efficient and less prone to overfitting (e.g., in image processing or text data).
  • Principal Component Analysis (PCA) – PCA reduces dimensionality by transforming data into principal components that retain the most variance.
  • t-SNE (t-distributed Stochastic Neighbor Embedding) – Used for visualizing high-dimensional data in lower dimensions (2D or 3D), it preserves local structure and relationships between points.

Gradient Boosting Machines (GBM)

  • Use Case: Popular in competitions like Kaggle and for ranking, classification, and regression (e.g., predicting customer churn and house prices).
  • Boosting Algorithm Technique—Similar to AdaBoost, GBM uses gradient descent to minimize the loss function, focusing on improving the predictions of prior models by correcting errors in a gradient-based manner.
  • Flexible Customization – Supports a wide variety of loss functions (e.g., squared error for regression or log loss for classification), allowing customization for specific problems.

But just the popular ML algorithms, we also have a list of chatbots. Read the future of chatbots: trends to watch in 2024 to know about it all. 

How To Choose The Best Machine Learning Algorithms Tools And Techniques For Data Annotation?

Images shows how to choose the best machine learning algorithms

Machine learning in healthcare has been helping overcome challenges. Therefore, careful utilization and making the perfect choice is essential. To choose the best machine learning tools for Data Annotation Services, follow the given steps:

Understanding Data Annotation Types

Text Annotation: Essential for NLP tasks such as:

  • Text Classification: Categorizing documents (e.g., spam vs. non-spam).
  • Named Entity Recognition (NER): Identifying specific entities such as names and dates.
  • Sentiment Analysis: Classifying sentiments in text (positive, negative, neutral).

Image Annotation: Involves labeling and marking objects within images, using bounding boxes and polygons techniques to train AI models for object detection tasks.

Audio and Video Annotation: This feature provides built-in capabilities for delivering custom annotations for unstructured transcripts of audio clips and video frames for speech recognition and action recognition using the latest machine learning techniques.

Choosing the Right Tools

In your decision of data annotation tools, consider the types of data they support, the level of automation features they have available to them, usability and ease-of-use functions, quality control mechanisms built into the tool itself, and collaboration functionality. Data scientists must have reliable data to perform the different machine learning algorithms. Hence, these functionalities are needed to get the most from the machine learning capabilities.

Future Trends: Large action models in machine learning help AI make smart decisions and take action.

Evaluating Performance Metrics

Then, you will test the tool to see if it fulfills your needs. So, make sure to have Accuracy, Precision & mAP. Accuracy is useful in balancing with the True Positive rate to align with ground truth data, Precision shows how much relevant data was generated, and mean average precision (mAP) makes sure that object detection should be comprehensive.

Conclusion

As machine learning continues to evolve, knowing the machine learning algorithms helps to ensure accurate training data, which in turn ensures accuracy and precision in the annotator’s work. We hope this blog will help you understand the basics well and keep you ahead of the curve.

Wichert Bruining