- Top 10 Kaggle Machine Learning Projects to Become Data Scientist in 2024 - Dec 7, 2023.
Master Data Science with Top 10 Kaggle ML Projects to become a Data Scientist.
- WTF is the Difference Between GBM and XGBoost? - Dec 6, 2023.
See the substantial differences between the famous algorithms.
- Beyond Guesswork: Leveraging Bayesian Statistics for Effective Article Title Selection - Dec 5, 2023.
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.
- 10 GitHub Repositories to Master Machine Learning - Dec 1, 2023.
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.
- Free MIT Course: TinyML and Efficient Deep Learning Computing - Dec 1, 2023.
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!
- Building Predictive Models: Logistic Regression in Python - Dec 1, 2023.
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.
- Building a GPU Machine vs. Using the GPU Cloud - Nov 29, 2023.
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.
- 5 Free Courses to Master Machine Learning - Nov 23, 2023.
Are you excited to learn about and build machine learning models? Start learning today with these free machine learning courses.
- Back to Basics Week 3: Introduction to Machine Learning - Nov 20, 2023.
Welcome back to Week 3 of KDnuggets’ "Back to Basics" series. This week, we will be diving into the world of machine learning.
- Hyperparameter Tuning: GridSearchCV and RandomizedSearchCV, Explained - Nov 3, 2023.
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.
- 7 Machine Learning Algorithms You Can’t Miss - Nov 1, 2023.
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.
- Overview of PEFT: State-of-the-art Parameter-Efficient Fine-Tuning - Oct 26, 2023.
Learn how Parameter-Efficient Fine-Tuning techniques like LoRA enable efficient adaptation of large language models using limited compute resources.
- The Top 5 Cloud Machine Learning Platforms & Tools - Oct 25, 2023.
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.
- 5 Free Books to Master Machine Learning - Oct 25, 2023.
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.
- How Predictive Analytics is Revolutionizing Decision-Making in Tech - Oct 24, 2023.
Learn how predictive analytics work in a business environment.
- A Brief History of the Neural Networks - Oct 20, 2023.
From the biological neuron to LLMs: How AI became smart.
- Gradient Descent: The Mountain Trekker’s Guide to Optimization with Mathematics - Oct 19, 2023.
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.
- Rust Burn Library for Deep Learning - Oct 13, 2023.
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.
- Customer Segmentation in Python: A Practical Approach - Oct 10, 2023.
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.
- Understanding Classification Metrics: Your Guide to Assessing Model Accuracy - Oct 10, 2023.
Navigating the Maze of Accuracy, Precision, and Recall in Machine Learning.
- Unveiling Hidden Patterns: An Introduction to Hierarchical Clustering - Oct 6, 2023.
In this guide to hierarchical clustering, learn how agglomerative and divisive clustering algorithms work. Also build a hierarchical clustering model in Python using Scipy.
- The Quest for Model Confidence: Can You Trust a Black Box? - Oct 2, 2023.
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.
- Getting Started with PyTorch in 5 Steps - Sep 29, 2023.
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.
- Diving into the Pool: Unraveling the Magic of CNN Pooling Layers - Sep 28, 2023.
A Beginner's Guide to Max, Average, and Global Pooling in Convolutional Neural Networks.
- Building a Convolutional Neural Network with PyTorch - Sep 25, 2023.
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.
- Introduction to Deep Learning Libraries: PyTorch and Lightning AI - Sep 23, 2023.
Simple explanation of PyTorch and Lightning AI.
- Exploring Neural Networks - Sep 22, 2023.
Unlocking the power of AI: a suide to neural networks and their applications.
- Your Features Are Important? It Doesn’t Mean They Are Good - Sep 21, 2023.
“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.
- Machine Learning Evaluation Metrics: Theory and Overview - Sep 21, 2023.
High-level exploration of evaluation metrics in machine learning and their importance.
- Hands-On with Unsupervised Learning: K-Means Clustering - Sep 20, 2023.
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.
- Unveiling Neural Magic: A Dive into Activation Functions - Sep 19, 2023.
Cracking the code of activation functions: Demystifying their purpose, selection, and timing.
- Unveiling Unsupervised Learning - Sep 19, 2023.
Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.
- Ensemble Learning Techniques: A Walkthrough with Random Forests in Python - Sep 18, 2023.
A practical walkthrough for random forests in Python.
- Hands-On with Supervised Learning: Linear Regression - Sep 18, 2023.
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.
- Understanding Supervised Learning: Theory and Overview - Sep 17, 2023.
This article covers a high-level overview of popular supervised learning algorithms and is curated specially for beginners.
- Getting Started with Scikit-learn in 5 Steps - Sep 16, 2023.
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.
- Demystifying Machine Learning - Sep 15, 2023.
This article is intended to familiarize you with the essence of Machine learning, the basic concepts, and the high-level machine learning process.
- Linear Regression from Scratch with NumPy - Sep 14, 2023.
Mastering the Basics of Linear Regression and Fundamentals of Gradient Descent and Loss Minimization.
- Scikit-learn for Machine Learning Cheat Sheet - Sep 13, 2023.
The latest KDnuggets exclusive cheatsheet covers the essentials of machine learning with Scikit-learn.
- Understanding Machine Learning Algorithms: An In-Depth Overview - Sep 12, 2023.
Understanding Machine Learning: Exposing the Tasks, Algorithms, and Selecting the Best Model.
- From Zero to Hero: Create Your First ML Model with PyTorch - Sep 11, 2023.
Learn the PyTorch basics by building a classification model from scratch.
- PyTorch Tips to Boost Your Productivity - Aug 23, 2023.
Master PyTorch with these proven methods.
- Leveraging XGBoost for Time-Series Forecasting - Aug 22, 2023.
Enabling the powerful algorithm to forecast from your data.
- Text-2-Video Generation: Step-by-Step Guide - Aug 17, 2023.
Bringing Words to Life: Easy Techniques to Generate Stunning Videos from Text Using Python.
- Time Series Analysis: ARIMA Models in Python - Aug 9, 2023.
ARIMA models are a popular tool for time series forecasting, and can be implemented in Python using the `statsmodels` library.
- Multilabel Classification: An Introduction with Python’s Scikit-Learn - Aug 4, 2023.
Learn how to develop Multilabel Classifier in your work.
- Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning are Transforming Machine Learning - Aug 3, 2023.
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.
- Using SHAP Values for Model Interpretability in Machine Learning - Aug 2, 2023.
Discover how SHAP can help you understand the impact of model features on predictions.
- Multivariate Time-Series Prediction with BQML - Jul 31, 2023.
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.
- Keras 3.0: Everything You Need To Know - Jul 31, 2023.
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.
- LGBMClassifier: A Getting Started Guide - Jul 29, 2023.
This tutorial explores the LightGBM library in Python to build a classification model using the LGBMClassifier class.
- Clustering Unleashed: Understanding K-Means Clustering - Jul 26, 2023.
Learn how to find hidden patterns and extract meaningful insights using Unsupervised Learning with the K-Means clustering algorithm.
- Unlock the Secrets to Choosing the Perfect Machine Learning Algorithm! - Jul 24, 2023.
When working on a data science problem, one of the most important choices to make is selecting the appropriate machine learning algorithm.
- A Practical Approach To Feature Engineering In Machine Learning - Jul 14, 2023.
This article discussed the importance of feature learning in machine learning and how it can be implemented in simple, practical steps.
- A Gentle Introduction to Support Vector Machines - Jul 10, 2023.
A guide to understanding support vector machines for classification: from theory to scikit-learn implementation.
- Reinforcement Learning: Teaching Computers to Make Optimal Decisions - Jul 7, 2023.
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.
- Introduction to Safetensors - Jul 6, 2023.
Introducing a new tool that offers speed, efficiency, cross-platform compatibility, user-friendliness, and security for deep learning applications.
- Overcoming Imbalanced Data Challenges in Real-World Scenarios - Jul 6, 2023.
Techniques to address imbalanced data in the context of classification, while keeping the data distribution in mind.
- Data Science Project of Rotten Tomatoes Movie Rating Prediction: Second Approach - Jul 5, 2023.
Predicting Movie Status Based on Review Sentiment.
- Data Scaling with Python - Jul 4, 2023.
How to scale your data to render it suitable for model building.
- Data Science Project of Rotten Tomatoes Movie Rating Prediction: First Approach - Jun 28, 2023.
Predicting Movie Status Based on Numerical and Categorical Features.
- From Theory to Practice: Building a k-Nearest Neighbors Classifier - Jun 27, 2023.
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.
- The Importance of Reproducibility in Machine Learning - Jun 27, 2023.
And how approaches to better data management, version control, and experiment tracking can help build reproducible ML pipelines.
- Closing the Gap Between Human Understanding and Machine Learning: Explainable AI as a Solution - Jun 21, 2023.
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.
- Making Predictions: A Beginner’s Guide to Linear Regression in Python - Jun 21, 2023.
Learn everything about the most popular Machine Learning algorithm, Linear Regression, with its Mathematical Intuition and Python implementation.
- A Practical Guide to Transfer Learning using PyTorch - Jun 20, 2023.
In this article, we’ll learn to adapt pre-trained models to custom classification tasks using a technique called transfer learning. We will demonstrate it for an image classification task using PyTorch, and compare transfer learning on 3 pre-trained models, Vgg16, ResNet50, and ResNet152.
- Calculate Computational Efficiency of Deep Learning Models with FLOPs and MACs - Jun 19, 2023.
In this article we will learn about its definition, differences and how to calculate FLOPs and MACs using Python packages.
- A Comprehensive Guide to Convolutional Neural Networks - Jun 16, 2023.
Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. Researchers and enthusiasts alike, work on numerous aspects of the field to make amazing things happen. One of many such areas is the domain of Computer Vision.
- Vanishing Gradient Problem: Causes, Consequences, and Solutions - Jun 15, 2023.
This blog post aims to describe the vanishing gradient problem and explain how use of the sigmoid function resulted in it.
- There and Back Again… a RAPIDS Tale - Jun 14, 2023.
This blog post explores the challenges of acquiring sufficient data and the limitations posed by biased datasets using RapidsAI cuDF.
- Advanced Feature Selection Techniques for Machine Learning Models - Jun 6, 2023.
Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.
- The Top AutoML Frameworks You Should Consider in 2023 - May 31, 2023.
AutoML frameworks are powerful tool for data analysts and machine learning specialists that can automate data preprocessing, model selection, hyperparameter tuning, and even perform complex tasks like feature engineering.
- Deep Learning with R - May 30, 2023.
In this tutorial, learn how to perform a deep learning task in R.
- What Are Foundation Models and How Do They Work? - May 23, 2023.
Foundation models represent a significant advancement in AI, enabling versatile and high-performing models that can be applied across various domains, such as NLP, computer vision, and multimodal tasks.
- Principal Component Analysis (PCA) with Scikit-Learn - May 16, 2023.
Learn how to perform principal component analysis (PCA) in Python using the scikit-learn library.
- Clustering with scikit-learn: A Tutorial on Unsupervised Learning - May 11, 2023.
Clustering in machine learning with Python: algorithms, evaluation metrics, real-life applications, and more.
- What is K-Means Clustering and How Does its Algorithm Work? - May 2, 2023.
In this article, we’ll cover what K-Means clustering is, how the algorithm works, choosing K, and a brief mention of its applications.
- Building and Training Your First Neural Network with TensorFlow and Keras - May 2, 2023.
Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network.
- Machine Learning with ChatGPT Cheat Sheet - May 1, 2023.
Have you thought of using ChatGPT to help augment your machine learning tasks? Check out our latest cheat sheet to find out how.
- Unveiling the Potential of CTGAN: Harnessing Generative AI for Synthetic Data - Apr 20, 2023.
CTGAN and other generative AI models can create synthetic tabular data for ML training, data augmentation, testing, privacy-preserving sharing, and more.
- Exploring Unsupervised Learning Metrics - Apr 13, 2023.
Improves your data science skill arsenals with these metrics.
- Automated Machine Learning with Python: A Case Study - Apr 11, 2023.
How to Automate the Complete Lifecycle of a Data Science Project using AutoML tools, which reduces the programming effort for implementation with H2O.ai.
- Best Machine Learning Model For Sparse Data - Apr 7, 2023.
Sparse Data Survival Guide: Strategies for Success with Machine Learning.
- Announcing PyCaret 3.0: Open-source, Low-code Machine Learning in Python - Mar 30, 2023.
Exploring the Latest Enhancements and Features of PyCaret 3.0.
- 5 Machine Learning Skills Every Machine Learning Engineer Should Know in 2023 - Mar 28, 2023.
Most essential skills are programming, data preparation, statistical analysis, deep learning, and natural language processing.
- Top 15 YouTube Channels to Level Up Your Machine Learning Skills - Mar 23, 2023.
Machine learning is the key driver of innovation and progress but finding the right resources to learn can be a tiring process. Save time searching aimlessly, and take advantage of our curated list of the top 15 YouTube channels to jumpstart your journey.
- Machine Learning: What is Bootstrapping? - Mar 22, 2023.
Bootstrapping is an essential technique if you're into machine learning. We’ll discuss it from theoretical and practical standpoints. The practical part involves two examples of bootstrapping in Python.
- Automated Machine Learning with Python: A Comparison of Different Approaches - Mar 21, 2023.
These four automated machine learning tools will help you build ML models quickly for your Data Science projects.
- Gaussian Naive Bayes, Explained - Mar 20, 2023.
Learn how Gaussian Naive Bayes works and implement it in Python.
- Top Machine Learning Papers to Read in 2023 - Mar 17, 2023.
These curated papers would step up your machine-learning knowledge.
- Dealing with Position Bias in Recommendations and Search - Mar 14, 2023.
People click on top items in search and recommendations more often because they are on top, not because of their relevancy. How can this problem be solved?
- Back To Basics, Part Dos: Gradient Descent - Mar 13, 2023.
Explore the inner workings of the powerful optimization algorithm.
- First Open Source Implementation of DeepMind’s AlphaTensor - Mar 10, 2023.
The first open-source implementation of AlphaTensor has been released and opens the door for new developments to revolutionize the computational performance of deep learning models.
- Hydra Configs for Deep Learning Experiments - Mar 7, 2023.
This brief guide illustrates how to use the Hydra library for ML experiments, especially in the case of deep learning-related tasks, and why you need this tool to make your workflow easier.
- Time Series Forecasting with statsmodels and Prophet - Mar 7, 2023.
Easy forecast model development with the popular time series Python packages.
- Key Issues Associated with Classification Accuracy - Mar 6, 2023.
In this blog, we will unfold the key problems associated with classification accuracies, such as imbalanced classes, overfitting, and data bias, and proven ways to address those issues successfully.
- Machine Learning Algorithms Explained in Less Than 1 Minute Each - Feb 28, 2023.
Learn about some of the most well known machine learning algorithms in less than a minute each.
- Top 5 Advantages That CatBoost ML Brings to Your Data to Make it Purr - Feb 27, 2023.
This article outlines the advantages of CatBoost as a GBDTs for interpreting data sources that are highly categorical or contain missing data points.
- Free TensorFlow 2.0 Complete Course - Feb 23, 2023.
Are you a beginner python programmer aiming to make a career in Machine Learning? If yes, then you are at the right place! This FREE tutorial will give you a solid understanding of the foundations of Machine Learning and Neural Networks using TensorFlow 2.0.
- Importance of Pre-Processing in Machine Learning - Feb 20, 2023.
Learn how pre-processing improves the performance of machine learning models.
- Generalized and Scalable Optimal Sparse Decision Trees(GOSDT) - Feb 17, 2023.
A simple method to solve complex real-life problems.
- Linear Regression Model Selection: Balancing Simplicity and Complexity - Feb 17, 2023.
How to select the linear regression model with the right balance between simplicity and complexity.
- Simple NLP Pipelines with HuggingFace Transformers - Feb 16, 2023.
Transformers by HuggingFace is an all-encompassing library with state-of-the-art pre-trained models and easy-to-use tools.
- Building a Recommender System for Amazon Products with Python - Feb 9, 2023.
I built a recommender system for Amazon’s electronics category.
- How to Implement a Federated Learning Project with Healthcare Data - Feb 3, 2023.
Learn about Federated Learning and how you can use it in the healthcare sector.
- Understanding by Implementing: Decision Tree - Feb 2, 2023.
Learn how a Decision Tree works and implement it in Python.
- Learn Machine Learning From These GitHub Repositories - Jan 31, 2023.
Kickstart your Machine Learning career with these curated GitHub repositories.
- Streamlit for Machine Learning Cheat Sheet - Jan 31, 2023.
The latest cheat sheet from KDnuggets demonstrates how to use Streamlit for building machine learning apps. Download the quick reference now.
- 7 SMOTE Variations for Oversampling - Jan 27, 2023.
Best oversampling techniques for the imbalanced data.
- An Introduction to Markov Chains - Jan 26, 2023.
Markov chains are often used to model systems that exhibit memoryless behavior, where the system's future behavior is not influenced by its past behavior.
- Hyperparameter Optimization: 10 Top Python Libraries - Jan 26, 2023.
Become familiar with some of the most popular Python libraries available for hyperparameter optimization.
- 5 Ways to Deal with the Lack of Data in Machine Learning - Jan 24, 2023.
Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.
- Genetic Programming in Python: The Knapsack Problem - Jan 24, 2023.
This article explores the knapsack problem. We will discuss why it is difficult to solve traditionally and how genetic programming can help find a "good enough" solution. We will then look at a Python implementation of this solution to test out for ourselves.
- 7 Best Libraries for Machine Learning Explained - Jan 24, 2023.
Learn about machine learning libraries for building and deploying machine learning models.
- How to Use Python and Machine Learning to Predict Football Match Winners - Jan 18, 2023.
We will be learning web scraping and training supervised machine-learning algorithms to predict winning teams.
- Idiot’s Guide to Precision, Recall, and Confusion Matrix - Jan 17, 2023.
Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.
- Explainable AI: 10 Python Libraries for Demystifying Your Model’s Decisions - Jan 16, 2023.
Become familiar with some of the most popular Python libraries available for AI explainability.
- Approaches to Data Imputation - Jan 12, 2023.
This guide will discuss what data imputation is as well as the types of approaches it supports.
- The Fast and Effective Way to Audit ML for Fairness - Jan 5, 2023.
Is your model fair? Here's how to audit using the Aequitas Toolkit.
- Micro, Macro & Weighted Averages of F1 Score, Clearly Explained - Jan 4, 2023.
Understanding the concepts behind the micro average, macro average, and weighted average of F1 score in multi-class classification with simple illustrations.
- Introduction to Multi-Armed Bandit Problems - Jan 3, 2023.
Delve deeper into the concept of multi-armed bandits, reinforcement learning, and exploration vs. exploitation dilemma.
- Unsupervised Disentangled Representation Learning in Class Imbalanced Dataset Using Elastic Info-GAN - Jan 2, 2023.
This рареr attempts to exploit primarily twо flaws in the Infо-GАN рареr while retаining the оther good qualities improvements.
- 24 Best (and Free) Books To Understand Machine Learning - Dec 28, 2022.
We have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field.
- The Importance of Permutation in Neural Network Predictions - Dec 23, 2022.
Permutation plays a significant role in making neural networks work as expected and showing whether they provide valid results. Explore how it affects neural network predictions now.
- Getting Started with Scikit-learn for Classification in Machine Learning - Dec 21, 2022.
The tutorial will introduce you to the scikit-learn module and its various features. It will also give you a brief overview of the multiclass classification problem through various algorithms.
- 7 Super Cheat Sheets You Need To Ace Machine Learning Interview - Dec 19, 2022.
Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.
- Zero-shot Learning, Explained - Dec 16, 2022.
How you can train a model to learn and predict unseen data?
- Tuning Adam Optimizer Parameters in PyTorch - Dec 15, 2022.
Choosing the right optimizer to minimize the loss between the predictions and the ground truth is one of the crucial elements of designing neural networks.
- How to Set Yourself Apart from Other Applicants with Data-Centric AI - Dec 12, 2022.
This article is designed to help you prepare for the job market and get yourself noticed in the industry.
- 3 Free Machine Learning Courses for Beginners - Dec 8, 2022.
Begin your machine learning career with free courses by Georgia Tech, Stanford, and Fast AI.