Get up to speed with the 7 most essential machine learning algorithms. Perfect for beginners and busy devs who want a quick, clear overview.

Explore these top machine learning repositories to build your skills, portfolio, and creativity through hands-on projects, real-world challenges, and AI resources.

This article explains how to turn messy raw data into useful features that help machine learning models make smarter and more accurate predictions.

Stop leaving your models on your laptop. Serve them to the world with this quick and powerful setup.

MLFlow is a tool that helps you manage machine learning projects. It makes it easier to track experiments, save models, and deploy them.

Use these frameworks to optimize memory and compute resources, scale your machine learning workflow, speed up your processes, and reduce the overall cost.

Stay ahead in 2025 with the latest OCR models optimized for speed, accuracy, and versatility in handling everything from scanned documents to complex layouts.

Build and fine-tune XGBoost models entirely online — no installations, just data, tuning, and results inside your browser.

Amid so many different machine learning algorithms to choose from. This guide has been designed to help you navigate towards the right one for you, depending on your data and the problem to address.

Built an ML model? Here are 7 free platforms to share it with the world.

Discover the what, why, and how of serverless machine learning with the interactive course GitHub repository.

Learn about the AWS machine learning service that helps you build machine learning pipelines, from processing data to training and deploying models.

A step-by-step guide to securing a FastAPI machine learning applications' endpoints with native authentication and user management.

Are you interested in enhancing your machine learning skills? We have put together an outstanding list of free machine learning books to aid your learning journey!

Check out this essential guide to the difference between training and inference.

Want to build powerful machine learning models in minutes, not weeks? AutoGluon makes it ridiculously easy — no PhD required!

A list of machine learning conferences that can give you access to the the newest papers out there.

Machine learning has transformed industries with advanced predictive abilities, but achieving breakthroughs in causality will depend on overcoming practical and computational challenges.

Let’s learn how to move your model from development into production.

This guide will explore the ins and outs of cross-validation, examine its different methods, and discuss why it matters in today's data science and machine learning processes.

A simple guide for getting hooked to machine learning and building a successful career in the field.

Master essential data cleaning techniques to improve machine learning models, from handling missing data to feature selection, using a real-world project.

Keep your ML workflow organized! Pipelines are like a checklist you don’t have to keep track of—Scikit-Learn handles it all for you.

Here are five free resources in diverse formats and difficulty levels to acquaint with deep learning models at no cost.

Check out these key concepts, tools, jargon, and tips for integrating ML models into existing software systems.

Learn reinforcement learning using free resources, including books, frameworks, courses, tutorials, example code, and projects.

To help you navigate this complex subject, we’ve compiled five free online courses that will give you a solid foundation in machine learning algorithms.

Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article.

Tired of fixing the same deployment issues? Learn how Docker can keep your ML models running smoothly, every time.

Learn how to accurately forecast the Meta stock market prices using a few lines of code.

In this article, we will explore how machine learning has transformed disease risk prediction in healthcare and where future improvements can continue to be made.

Learn deep learning through a variety of free resources, including books, courses, tutorials, model implementations, visualizations, and deployment, and Google Colab code examples.

Deep learning has made medical image segmentation much more accurate and efficient. This article will explore common techniques and their applications.

Learn to use the advanced models from Hugging Face.

Learn about machine learning APIs for datasets, models, web applications, free GPUs, and text, audio, and image generation.

How does AI learn by doing? Read this to discover the basics of reinforcement learning.

Explainable AI (XAI) makes it easier to understand how AI decisions are made. This introduction explains what XAI is and why it matters.

Discover how decision trees simplify complex choices and enhance data-driven decisions in real-world scenarios.

Embrace these five best-practices boost the effectiveness of your trained machine learning solutions, no matter their complexity

The article highlights five top free machine learning courses to enhance your skills.

Learn how to build the recommendation system with advanced technology.

Uncover the potential of cluster analysis for segmenting, analyzing, and gaining insights from groups of similar data.

Transfer learning can improve model performance by leveraging pre-trained models and adapting them to new, related tasks.

Thanks to tools like BigQuery ML, you can harness the power of ML without needing a computer science degree. Let's explore how to get started.

Tips and tricks on improving machine learning model performance on diverse and unseen datasets.

Learn essential techniques for tuning hyperparameters to enhance the performance of your neural networks.

Let us explore the process of deploying models in production.

Perform data integrity tests and generate model evaluation reports by writing a few lines of code.

Machine Learning model is an exciting project. Learn how to develop your first model that the company would want to use.

Learn about the most popular machine learning models, understand how they work, and discover the best free courses to master them.

Master the Fundamentals of Predictive Modeling with Python: An In-Depth Guide to Machine Learning Algorithms and Sci-kit Learn Implementation.

This guide introduces some key techniques in the feature engineering process and provides practical examples in Python.

Want to learn machine learning from the best of resources? Check out these free machine learning courses from the top universities of the world.

Enrich your knowledge with these papers

The goal of this article is to help demystify the process of selecting the proper machine learning algorithm, concentrating on "traditional" algorithms and offering some guidelines for choosing the best one for your application.

Data science’s essence lies in machine learning algorithms. Here are ten algorithms that are a great introduction to machine learning for any beginner!

Dive into the 50 most popular deep-learning questions to get you ready for your interview.

Want to become an ML engineer? These free courses from Google can help you get there.

This article explains the concept of regularization and its significance in machine learning and deep learning. We have discussed how regularization can be used to enhance the performance of linear models, as well as how it can be applied to improve the performance of deep learning models.

Learn how to enhance the quality of your machine learning code using Scikit-learn Pipeline and ColumnTransformer.

The podcasts that would improve your ML knowledge.

You don't always have high-quality labeled datasets for supervised machine learning. Learn about why you should augment your real data with synthetic data as well as the ways to generate it.

Machine Learning is a skill that everyone should have, and these cheap books would facilitate that learning process.

Never underestimate any algorithms that we can use.

Eric Siegel highlights the chronic under-deployment of ML projects, with only 22% of data scientists saying their revolutionary initiatives usually deploy, and a lack of stakeholder visibility and detailed planning as key issues, in his industry survey and book "The AI Playbook."

Master Data Science with Top 10 Kaggle ML Projects to become a Data Scientist.

See the substantial differences between the famous algorithms.

The article discusses how Bayesian multi-armed bandit algorithms can optimize digital media title selection, surpassing traditional A/B testing methods, demonstrated with a Python example, to boost audience engagement and decision-making in content creation.

The blog covers machine learning courses, bootcamps, books, tools, interview questions, cheat sheets, MLOps platforms, and more to master ML and secure your dream job.

Curious about optimizing AI for everyday devices? Dive into the complete overview of MIT's TinyML and Efficient Deep Learning Computing course. Explore strategies to make AI smarter on small devices. Read the full article for an in-depth look!

Want to learn how to build predictive models using logistic regression? This tutorial covers logistic regression in depth with theory, math, and code to help you build better models.

The article examines the pros and cons of building an on-premise GPU machine versus using a GPU cloud service for projects involving deep learning and artificial intelligence, analyzing factors like cost, performance, operations, and scalability.

Are you excited to learn about and build machine learning models? Start learning today with these free machine learning courses.

Welcome back to Week 3 of KDnuggets’ "Back to Basics" series. This week, we will be diving into the world of machine learning.

Learn how to tune your model’s hyperparameters using grid search and randomized search. Also learn to implement them in scikit-learn using GridSearchCV and RandomizedSearchCV.

This list of machine learning algorithms is a good place to start your journey as a data scientist. You should be able to identify the most common models and use them in the right applications.

Learn how Parameter-Efficient Fine-Tuning techniques like LoRA enable efficient adaptation of large language models using limited compute resources.

What are the top 5 cloud machine learning platforms in the market today. Our list will help provide some vital insights into which platform might best cater to your specific machine learning needs. See what KDnuggets recommends.

Machine Learning is one of the most exciting fields in computer science today. In this article, we will take a look at the five best yet free books to learn machine learning in 2023.

Learn how predictive analytics work in a business environment.

From the biological neuron to LLMs: How AI became smart.

Gradient descent is an optimization technique used to minimise errors in machine learning models. By iteratively adjusting parameters in the steepest direction of decrease, it seeks the lowest error value.

A new deep learning framework built entirely in Rust that aims to balance flexibility, performance, and ease of use for researchers, ML engineers, and developers.

So you want to understand your customer base better? Learn how to leverage RFM analysis and K-Means clustering in Python to perform customer segmentation.

Navigating the Maze of Accuracy, Precision, and Recall in Machine Learning.

In this guide to hierarchical clustering, learn how agglomerative and divisive clustering algorithms work. Also build a hierarchical clustering model in Python using Scipy.

This article explores strategies for evaluating the reliability of labels generated by Large Language Models (LLMs). It discusses the effectiveness of different approaches and offers practical insights for various applications.

This tutorial provides an in-depth introduction to machine learning using PyTorch and its high-level wrapper, PyTorch Lightning. The article covers essential steps from installation to advanced topics, offering a hands-on approach to building and training neural networks, and emphasizing the benefits of using Lightning.

A Beginner's Guide to Max, Average, and Global Pooling in Convolutional Neural Networks.

This blog post provides a tutorial on constructing a convolutional neural network for image classification in PyTorch, leveraging convolutional and pooling layers for feature extraction as well as fully-connected layers for prediction.

Simple explanation of PyTorch and Lightning AI.

Unlocking the power of AI: a suide to neural networks and their applications.

“Feature Importance” is not enough. You also need to look at “Error Contribution” if you want to know which features are beneficial for your model.

High-level exploration of evaluation metrics in machine learning and their importance.

This tutorial provides hands-on experience with the key concepts and implementation of K-Means clustering, a popular unsupervised learning algorithm, for customer segmentation and targeted advertising applications.

Cracking the code of activation functions: Demystifying their purpose, selection, and timing.

Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.

A practical walkthrough for random forests in Python.

If you're looking for a hands-on experience with a detailed yet beginner-friendly tutorial on implementing Linear Regression using Scikit-learn, you're in for an engaging journey.

This article covers a high-level overview of popular supervised learning algorithms and is curated specially for beginners.

This tutorial offers a comprehensive hands-on walkthrough of machine learning with Scikit-learn. Readers will learn key concepts and techniques including data preprocessing, model training and evaluation, hyperparameter tuning, and compiling ensemble models for enhanced performance.

This article is intended to familiarize you with the essence of Machine learning, the basic concepts, and the high-level machine learning process.

Mastering the Basics of Linear Regression and Fundamentals of Gradient Descent and Loss Minimization.

The latest KDnuggets exclusive cheatsheet covers the essentials of machine learning with Scikit-learn.

Understanding Machine Learning: Exposing the Tasks, Algorithms, and Selecting the Best Model.

Learn the PyTorch basics by building a classification model from scratch.

Master PyTorch with these proven methods.

Enabling the powerful algorithm to forecast from your data.

Bringing Words to Life: Easy Techniques to Generate Stunning Videos from Text Using Python.

ARIMA models are a popular tool for time series forecasting, and can be implemented in Python using the `statsmodels` library.

Learn how to develop Multilabel Classifier in your work.

Discover the concepts of Zero-Shot, One-Shot, and Few-Shot Learning, which enable machine learning models to classify and recognize objects or patterns with a limited number of examples.

Discover how SHAP can help you understand the impact of model features on predictions.

Google's BQML can be used to make time series models, and recently it was updated to create multivariate time series models. With the simple code, this article shows how to use it to predict multivariate time series and it can be more powerful than a univariate time series model in this article.

Unlock the power of AI collaboration with Keras 3.0! Seamlessly switch between TensorFlow, JAX, and PyTorch, revolutionizing your deep learning projects. Read now and stay ahead in the world of AI.

This tutorial explores the LightGBM library in Python to build a classification model using the LGBMClassifier class.

Learn how to find hidden patterns and extract meaningful insights using Unsupervised Learning with the K-Means clustering algorithm.

When working on a data science problem, one of the most important choices to make is selecting the appropriate machine learning algorithm.

This article discussed the importance of feature learning in machine learning and how it can be implemented in simple, practical steps.

A guide to understanding support vector machines for classification: from theory to scikit-learn implementation.

Reinforcement learning basics to get your feet wet. Learn the components and key concepts in the reinforcement learning framework: from agents and rewards to value functions, policy, and more.

Introducing a new tool that offers speed, efficiency, cross-platform compatibility, user-friendliness, and security for deep learning applications.

Techniques to address imbalanced data in the context of classification, while keeping the data distribution in mind.

Predicting Movie Status Based on Review Sentiment.

How to scale your data to render it suitable for model building.

Predicting Movie Status Based on Numerical and Categorical Features.

The k-Nearest Neighbors Classifier is a machine learning algorithm that assigns a new data point to the most common class among its k closest neighbors. In this tutorial, you will learn the basic steps of building and applying this classifier in Python.

And how approaches to better data management, version control, and experiment tracking can help build reproducible ML pipelines.

This article elaborates on the importance of Explainable AI (XAI), what the challenges in building interpretable AI models are, and some practical guidelines for companies to build XAI models.

Learn everything about the most popular Machine Learning algorithm, Linear Regression, with its Mathematical Intuition and Python implementation.