Machine Learning

12 Best Machine Learning Algorithms of 2024

  Updated 29 Apr 2024

Transforming Healthcare

Machine learning(ML) algorithms are no short of a miracle for navigating the fast-paced and continually changing modern business environment.

One of the greatest gifts of the new age of technological advancements, Machine Learning Development Services serve as powerful tools, that tackle unique challenges in delivering the best efficiency, experience, and intelligence to your business.

They can recognize and derive patterns from vast amounts of otherwise unstructured or even scattered data without needing extensive explicit programming to produce meaningful and actionable suggestions much faster and more efficiently than is humanly possible.

This article lists the best machine learning algorithms available today, a basic summary of their capability, and the types of usage they are best suited for, to give you the information needed to choose the right tool for the right job.

12 Top Machine Learning Algorithms to Watch Out For

Algorithm Learning Algorithm Type What It Does Use Cases
XG Boost Supervised
  • Provides efficient and scalable library to train ML models
  • Combines predictions from multiple models
  • Efficient in handling of missing values without significant pre-processing
  • Highly accurate boosting algorithm
  • Highly customisable
  • Recommendation systems and click-through rate prediction
  • Classification and prediction tasks like text classification and sentiment analysis.
Linear Regression Supervised
  • Predicts the relationship between variables
  • Assumes a linear relationship between dependent variables or multiple independent variables.
  • Helps forecast the value of the dependent variable based on known data.
  • To resolve binary classification problems and predictive analysis.
  • Predicting the stock market
  • Forecasting trends for demand, production, etc.
Logistic Regression Supervised
  • Analyzes the relationship between multiple independent variables
  • Classifies data into binary categories(0 or 1)
  • Predicts the likelihood of outcomes falling into a certain category or group based on independent variables
  • Binary classification tasks
  • Predicting the probability of certain outcomes.
  • Image recognition, fraud detection, credit scoring, and predictive diagnosis.
Decision Tree Supervised
  • Uses a hierarchical tree structure
  • Used to classify data and predict outcomes.
  • Provide transparency through clear reasoning for data categorisation
  • Predictive modelling, regression, and classification tasks.
  • Predict customer behaviour, price movements, and diagnosis
Support Vector Machine (SVM) Supervised
  • Identifies a hyperplane: A crucial decision boundary to separate data into different classes.
  • Ignores the outlier and finds the best hyperplane
  • Uses kernel functions for data not linearly separable.
  • Makes classification and recognition tasks more accurate
  • Used in linear or nonlinear classification, regression, and outlier detection tasks
  • Text and image classification, facial recognition, and anomaly detection
-Nearest Neighbor (KNN) Supervised
  • It is non-parametric
  • Based on training data, find the “closest neighbour” to which data points can be allocated.
  • Can handle both numerical and categorical data
  • Customizable K value to determine the number of neighbours considered for classification of the output
  • Ideal for recommendation engines as it can assign new query points to a pre-existing group.
  • Pattern recognition, data processing, and intrusion detection.
K-Means Unsupervised
  • Groups data points together in clusters based on certain similarities
  • Data points in each group are more comparable to each other and different from those in other groups
  • Assigns points to clusters based on proximity to centroids which are kept as small as possible
  • Ideal for market and customer segmentation
  • Predictive modelling, behavioural analysis, and fraud detection
Naive Bayes Supervised
  • Uses Bayes’ theorem for calculating conditional probabilities
  • Additional information impacts the initial probability
  • Probabilities are denoted as prior and posterior probability
  • Predictors are conditionally independent and all features contribute to the outcome equally
  • Classification tasks like text and document classification, spam filtering, and sentiment analysis
  • Recently used to identify cognitive states, particularly for patients suffering brain injuries
Gradient Boosting Supervised
  • Combine predictions of multiple weak learning models to form strong cleaners
  • Each new model minimizes the loss function of the previous using gradient descent
  • Uses “Shrinkage” – prediction of each tree is shrunk after it is multiplied by the learning rate
  • Classification, ranking, and regression tasks
  • Best for processing large, complex datasets.
Stochastic Gradient Descent Unsupervised
  • Updates model parameters by taking a random data sample and calculating the gradient
  • It is efficient in dealing with large datasets
  • Greatly reduces the computational cost per iteration
  • It is an inexact but powerful technique and is used widely
  • Can help build various neural networks and ML models mentioned above
  • Provides a framework for refining the parameters of the models
Random Forest Supervised
  • Combines the output of multiple decision trees to return a single, strong result
  • Hyperparameters (node size, the number of trees, and the number of features) must be set before training
  • It reduces time spent on data management and pre-processing tasks but is still a more time-consuming process
  • Bagging is used to reduce variance in noisy data sets
  • Used in recommendation engines and to solve classification and regression problems.
  • Risk monitoring, fraud detection, and pricing
Apriori Unsupervised
  • Uses prior knowledge of frequent itemset properties
  • Assumes that “all subsets of a frequent itemset must be frequent(Apriori property). If an itemset is infrequent, all its supersets will be infrequent.”
  • Designed to work on databases containing transactions
  • An iterative approach where k-frequent itemsets are used to find k+1 itemsets.
  • Identifying frequent or repetitive itemsets in a dataset
  • To mine and analyze datasets
  • In recommender systems and autocomplete features(used by Google)

Machine Learning Development With Q3 Technologies

Machine learning development services available at Q3 Technologies help you transform how users engage with your business by building personalized user experiences and automating internal business processes using new-age innovations.

The highly customizable and flexible solutions are developed to match your business needs by world-class experts trained in all the latest technologies the project could require.

The unique approach that makes Q3 an ideal machine learning development company are listed below –

Methodology

At Q3, we pride ourselves on our proficiency in Agile and DevOps methodologies, enabling us to consistently deliver top-notch products punctually, without fail.

Requirement Assessment

Leveraging the expertise of our seasoned team, Q3 conducts thorough assessments of your business requirements, charting out a clear roadmap for crafting solutions that align perfectly with your expectations.

24/7 Support

Our commitment to exceptional service extends beyond development, with our impressive post-development support ensuring that released solutions undergo continuous monitoring and debugging, ensuring seamless operations around the clock.

Let’s Wrap Up

As we wrap up our exploration of the 12 Top Machine Learning Algorithms shaping the landscape of 2024, it’s clear that the potential for innovation and advancement in this field knows no bounds. From time-tested classics to cutting-edge innovations, each algorithm offers unique capabilities that can revolutionize businesses across industries.

At Q3 Technologies, we understand the importance of harnessing the right tools and technologies to stay ahead in today’s dynamic market. Our expertise in Machine Learning development empowers businesses to leverage these algorithms effectively, unlocking unprecedented insights and driving growth.

Table of content
  • – 12 Best Machine Learning Algorithms to Watch Out For
  • – Machine Learning Development With Q3 Technologies
  • – Let’s Wrap Up

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