- 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.
- Memory Complexity with Transformers - Dec 7, 2022.
What’s the problem with running a transformer model on a book with 1 million tokens? What can be a solution to this problem?
- The Complete Machine Learning Study Roadmap - Dec 7, 2022.
Find out where you need to be to start your Machine Learning journey and what you need to do to succeed in the field.
- How Machine Learning Can Benefit Online Learning - Dec 2, 2022.
Personalized learning, smart grading, skill gap assessment, and better ROI: The importance of incorporating Machine Learning in Online Learning cannot be overstated.
- Getting Started with PyTorch Lightning - Dec 1, 2022.
Introduction to PyTorch Lightning and how it can be used for the model building process. It also provides a brief overview of the PyTorch characteristics and how they are different from TensorFlow.
- Scikit-learn for Machine Learning Cheatsheet - Dec 1, 2022.
The latest KDnuggets exclusive cheatsheet covers the essentials of machine learning with Scikit-learn.
- Comparing Linear and Logistic Regression - Nov 29, 2022.
Discussion on an entry-level data science interview question.
- An Introduction to SMOTE - Nov 29, 2022.
Improve the model performance by balancing the dataset using the synthetic minority oversampling technique.
- SHAP: Explain Any Machine Learning Model in Python - Nov 21, 2022.
A Comprehensive Guide to SHAP and Shapley Values
- Picking Examples to Understand Machine Learning Model - Nov 21, 2022.
Understanding ML by combining explainability and sample picking.
- How LinkedIn Uses Machine Learning To Rank Your Feed - Nov 14, 2022.
In this post, you will learn to clarify business problems & constraints, understand problem statements, select evaluation metrics, overcome technical challenges, and design high-level systems.
- Machine Learning from Scratch: Decision Trees - Nov 11, 2022.
A simple explanation and implementation of DTs ID3 algorithm in Python
- Getting Started with PyCaret - Nov 10, 2022.
An open-source low-code machine learning library for training and deploying the models in production.
- Confusion Matrix, Precision, and Recall Explained - Nov 9, 2022.
Learn these key machine learning performance metrics to ace data science interviews.
- The Most Comprehensive List of Kaggle Solutions and Ideas - Nov 8, 2022.
Learn from top-performing teams in the competition to get better at understanding machine learning techniques.
- 15 More Free Machine Learning and Deep Learning Books - Nov 7, 2022.
Check out this second list of 15 FREE ebooks for learning machine learning and deep learning.
- Simple and Fast Data Streaming for Machine Learning Projects - Nov 3, 2022.
Learn about the cutting-edge DagsHub's Direct Data Access for simple and faster data loading and model training.
- Random Forest vs Decision Tree: Key Differences - Nov 1, 2022.
Check out this reasoned comparison of 2 critical machine learning algorithms to help you better make an informed decision.
- The Gap Between Deep Learning and Human Cognitive Abilities - Oct 31, 2022.
How do we bridge this gap between deep learning and human cognitive ability?
- 15 Free Machine Learning and Deep Learning Books - Oct 31, 2022.
Check out this list of 15 FREE ebooks for learning machine learning and deep learning.
- Machine Learning on the Edge - Oct 27, 2022.
Edge ML involves putting ML models on consumer devices where they can independently run inferences without an internet connection, in real-time, and at no cost.
- The First ML Value Chain Landscape - Oct 24, 2022.
TheSequence recently released the first ever ML Chain Landscape shaped by data scientists, a new landscape that would be able to address the entire ML value chain.
- Ensemble Learning with Examples - Oct 24, 2022.
Learn various algorithms to improve the robustness and performance of machine learning applications. Furthermore, it will help you build a more generalized and stable model.
- Why TinyML Cases Are Becoming Popular? - Oct 20, 2022.
This article will provide an overview of what TinyML is, its use cases, and why it is becoming more popular.
- Frameworks for Approaching the Machine Learning Process - Oct 19, 2022.
This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
- Working With Sparse Features In Machine Learning Models - Oct 17, 2022.
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
- Implementing Adaboost in Scikit-learn - Oct 17, 2022.
It is called Adaptive Boosting due to the fact that the weights are re-assigned to each instance, with higher weights being assigned to instances that are not correctly classified - therefore it ‘adapts’.
- Mathematics for Machine Learning: The Free eBook - Oct 14, 2022.
Check out this free ebook covering the fundamentals of mathematics for machine learning, as well as its companion website of exercises and Jupyter notebooks.
- Classification Metrics Walkthrough: Logistic Regression with Accuracy, Precision, Recall, and ROC - Oct 13, 2022.
In this article, I will be going through 4 common classification metrics: Accuracy, Precision, Recall, and ROC in relation to Logistic Regression.
- 5 Free Courses to Master Linear Algebra - Oct 13, 2022.
Linear Algebra is an important subfield of mathematics and forms a core foundation of machine learning algorithms. The post shares five free courses to master the concepts of linear algebra.
- The Complete Free PyTorch Course for Deep Learning - Oct 12, 2022.
Do you want to learn PyTorch for machine learning and deep learning? Check out this 24 hour long video course with accompanying notes and courseware for free. Did I mention it's free?
- A Day in the Life of a Machine Learning Engineer - Oct 10, 2022.
What does a day in the life as a machine learning engineer look like for you?
- Hyperparameter Tuning Using Grid Search and Random Search in Python - Oct 5, 2022.
A comprehensive guide on optimizing model hyperparameters with Scikit-Learn.
- Machine Learning for Everybody! - Oct 4, 2022.
Who is machine learning for? Everybody!
- Which Metric Should I Use? Accuracy vs. AUC - Oct 4, 2022.
Depending on the problem you’re trying to solve, one metric may be more insightful than another.
- 7 Steps to Mastering Machine Learning with Python in 2022 - Sep 30, 2022.
Are you trying to teach yourself machine learning from scratch, but aren’t sure where to start? I will attempt to condense all the resources I’ve used over the years into 7 steps that you can follow to teach yourself machine learning.
- Master Transformers with This Free Stanford Course! - Sep 30, 2022.
If you want a deep dive on transformers, this Stanford course has made its courseware freely available, including lecture videos, readings, assignments, and more.
- Beyond Pipelines: Graphs as Scikit-Learn Metaestimators - Sep 29, 2022.
Create manageable and scalable machine learning workflows with skdag.
- Top 5 Machine Learning Practices Recommended by Experts - Sep 28, 2022.
This article is intended to help beginners improve their model structure by listing the best practices recommended by machine learning experts.
- How to Correctly Select a Sample From a Huge Dataset in Machine Learning - Sep 27, 2022.
We explain how choosing a small, representative dataset from a large population can improve model training reliability.
- More Performance Evaluation Metrics for Classification Problems You Should Know - Sep 20, 2022.
When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.
- AWS AI & ML Scholarship Program Overview - Sep 19, 2022.
This scholarship program aims to help people who are underserved and that were underrepresented during high school and college - to then help them learn the foundations and concepts of Machine Learning and build a careers in AI and ML.
- 7 Machine Learning Portfolio Projects to Boost the Resume - Sep 19, 2022.
Work on machine learning and deep learning portfolio projects to learn new skills and improve your chance of getting hired.
- 5 Concepts You Should Know About Gradient Descent and Cost Function - Sep 16, 2022.
Why is Gradient Descent so important in Machine Learning? Learn more about this iterative optimization algorithm and how it is used to minimize a loss function.
- An Intuitive Explanation of Collaborative Filtering - Sep 15, 2022.
The post introduces one of the most popular recommendation algorithms, i.e., collaborative filtering. It focuses on building an intuitive understanding of the algorithm illustrated with the help of an example.
- Everything You’ve Ever Wanted to Know About Machine Learning - Sep 9, 2022.
Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike.
- Machine Learning Algorithms – What, Why, and How? - Sep 7, 2022.
This post explains why and when you need machine learning and concludes by listing the key considerations for choosing the correct machine learning algorithm.
- Choosing the Right Clustering Algorithm for Your Dataset - Sep 7, 2022.
Applying a clustering algorithm is much easier than selecting the best one. Each type offers pros and cons that must be considered if you’re striving for a tidy cluster structure.
- Visualizing Your Confusion Matrix in Scikit-learn - Sep 6, 2022.
Defining model evaluation metrics is crucial in ensuring that the model performs precisely for the purpose it is built. Confusion Matrix is one of the most popular and effective tools to evaluate the performance of the trained ML model. In this post, you will learn how to visualize the confusion matrix and interpret its output.
- Decision Tree Pruning: The Hows and Whys - Sep 2, 2022.
Decision trees are a machine learning algorithm that is susceptible to overfitting. One of the techniques you can use to reduce overfitting in decision trees is pruning.
- The Difference Between Training and Testing Data in Machine Learning - Aug 31, 2022.
When building a predictive model, the quality of the results depends on the data you use. In order to do so, you need to understand the difference between training and testing data in machine learning.
- Machine Learning Metadata Store - Aug 31, 2022.
In this article, we will learn about metadata stores, the need for them, their components, and metadata store management.
- A Complete Guide To Decision Tree Software - Aug 26, 2022.
Decision tree models are used to classify information into meaningful sequential results. Find out everything else you need to know here.
- How to Package and Distribute Machine Learning Models with MLFlow - Aug 25, 2022.
MLFlow is a tool to manage the end-to-end lifecycle of a Machine Learning model. Likewise, the installation and configuration of an MLFlow service is addressed and examples are added on how to generate and share projects with MLFlow.
- 7 Techniques to Handle Imbalanced Data - Aug 24, 2022.
This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.
- The Bias-Variance Trade-off - Aug 24, 2022.
Understanding how these prediction errors work and how they can be used will help you build models that are not only accurate and perform well - but also avoid overfitting and underfitting.
- Support Vector Machines: An Intuitive Approach - Aug 23, 2022.
This post focuses on building an intuition of the Support Vector Machine algorithm in a classification context and an in-depth understanding of how that graphical intuition can be mathematically represented in the form of a loss function. We will also discuss kernel tricks and a more useful variant of SVM with a soft margin.
- Tuning Random Forest Hyperparameters - Aug 22, 2022.
Hyperparameter tuning is important for algorithms. It improves their overall performance of a machine learning model and is set before the learning process and happens outside of the model.
- Implementing DBSCAN in Python - Aug 17, 2022.
Density-based clustering algorithm explained with scikit-learn code example.
- How to Avoid Overfitting - Aug 17, 2022.
Overfitting is when a statistical model fits exactly against its training data. This leads to the model failing to predict future observations accurately.
- Machine Learning Over Encrypted Data - Aug 16, 2022.
This blog outlines a solution to the Kaggle Titanic challenge that employs Privacy-Preserving Machine Learning (PPML) using the Concrete-ML open-source toolkit.
- What Does ETL Have to Do with Machine Learning? - Aug 15, 2022.
ETL during the process of producing effective machine learning algorithms is found at the base - the foundation. Let’s go through the steps on how ETL is important to machine learning.
- Data Transformation: Standardization vs Normalization - Aug 12, 2022.
Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.
- Tuning XGBoost Hyperparameters - Aug 11, 2022.
Hyperparameter tuning is about finding a set of optimal hyperparameter values which maximizes the model's performance, minimizes loss, and produces better outputs.
- The Difference Between L1 and L2 Regularization - Aug 10, 2022.
Two types of regularized regression models are discussed here: Ridge Regression (L2 Regularization), and Lasso Regression (L1 Regularization)
- 6 Ways Businesses Can Benefit From Machine Learning - Aug 9, 2022.
Machine learning is gaining popularity rapidly in the business world. Discover the ways that your business can benefit from machine learning.
- How to Deal with Categorical Data for Machine Learning - Aug 4, 2022.
Check out this guide to implementing different types of encoding for categorical data, including a cheat sheet on when to use what type.
- What are the Assumptions of XGBoost? - Aug 4, 2022.
In this article, you will learn: how boosting relates to XGBoost; the features of XGBoost; how it reduces the loss function value and overfitting.
- Decision Trees vs Random Forests, Explained - Aug 2, 2022.
A simple, non-math heavy explanation of two popular tree-based machine learning models.
- How ML Model Explainability Accelerates the AI Adoption Journey for Financial Services - Jul 29, 2022.
Explainability and good model governance reduce risk and create the framework for ethical and transparent AI in financial services that eliminates bias.
- K-nearest Neighbors in Scikit-learn - Jul 28, 2022.
Learn about the k-nearest neighbours algorithm, one of the most prominent workhorse machine learning algorithms there is, and how to implement it using Scikit-learn in Python.
- Is Domain Knowledge Important for Machine Learning? - Jul 27, 2022.
If you incorporate domain knowledge into your architecture and your model, it can make it a lot easier to explain the results, both to yourself and to an outside viewer. Every bit of domain knowledge can serve as a stepping stone through the black box of a machine learning model.
- Detecting Data Drift for Ensuring Production ML Model Quality Using Eurybia - Jul 26, 2022.
This article will focus on a step-by-step data drift study using Eurybia an open-source python library
- Does the Random Forest Algorithm Need Normalization? - Jul 25, 2022.
Normalization is a good technique to use when your data consists of being scaled and your choice of machine learning algorithm does not have the ability to make assumptions on the distribution of your data.
- Using Scikit-learn’s Imputer - Jul 25, 2022.
Learn about Scikit-learn’s SimpleImputer, IterativeImputer, KNNImputer, and machine learning pipelines.
- Practical Deep Learning from fast.ai is Back! - Jul 25, 2022.
Looking for a great course to go from machine learning zero to hero quickly? fast.ai has released the latest version of Practical Deep Learning For Coders. And it won't cost you a thing.
- The Difficulty of Estimating the Carbon Footprint of Machine Learning - Jul 22, 2022.
Is machine learning killing the planet? Probably not, but let's make sure it doesn't.
- When Would Ensemble Techniques be a Good Choice? - Jul 18, 2022.
When would ensemble techniques be a good choice? When you want to improve the performance of machine learning models - it’s that simple.
- How Does Logistic Regression Work? - Jul 15, 2022.
Logistic regression is a machine learning classification algorithm that is used to predict the probability of certain classes based on some dependent variables
- Machine Learning Algorithms Explained in Less Than 1 Minute Each - Jul 13, 2022.
Learn about some of the most well known machine learning algorithms in less than a minute each.
- Why Use k-fold Cross Validation? - Jul 11, 2022.
Generalizing things is easy for us humans, however, it can be challenging for Machine Learning models. This is where Cross-Validation comes into the picture.
- Boosting Machine Learning Algorithms: An Overview - Jul 8, 2022.
The combination of several machine learning algorithms is referred to as ensemble learning. There are several ensemble learning techniques. In this article, we will focus on boosting.
- Ten Key Lessons of Implementing Recommendation Systems in Business - Jul 7, 2022.
We've been long working on improving the user experience in UGC products with machine learning. Following this article's advice, you will avoid a lot of mistakes when creating a recommendation system, and it will help to build a really good product.
- Linear Machine Learning Algorithms: An Overview - Jul 1, 2022.
In this article, we’ll discuss several linear algorithms and their concepts.
- Primary Supervised Learning Algorithms Used in Machine Learning - Jun 17, 2022.
In this tutorial, we are going to list some of the most common algorithms that are used in supervised learning along with a practical tutorial on such algorithms.
- Deep Learning Key Terms, Explained - Jun 13, 2022.
Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions.
- A Structured Approach To Building a Machine Learning Model - Jun 10, 2022.
This article gives you a glimpse of how to approach a machine learning project with a clear outline of an easy-to-implement 5-step process.
- How is Data Mining Different from Machine Learning? - Jun 8, 2022.
How about we take a closer look at data mining and machine learning so we know how to catch their different ends?
- Genetic Algorithm Key Terms, Explained - Jun 6, 2022.
This article presents simple definitions for 12 genetic algorithm key terms, in order to help better introduce the concepts to newcomers.
- How Activation Functions Work in Deep Learning - Jun 3, 2022.
Check out a this article for a better understanding of activation functions.
- A Beginner’s Guide to Q Learning - Jun 3, 2022.
Learn the basics of Q-learning in this article, a model-free reinforcement learning algorithm.
- How to Become a Machine Learning Engineer - May 30, 2022.
A machine learning engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, compared to data scientists.
- Weak Supervision Modeling, Explained - May 27, 2022.
This article dives into weak supervision modeling and truly understanding the label model.
- Operationalizing Machine Learning from PoC to Production - May 20, 2022.
Most companies haven’t seen ROI from machine learning since the benefit is only realized when the models are in production. Here’s how to make sure your ML project works.
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability - May 20, 2022.
We give a taxonomy of the trustworthy GNNs in privacy, robustness, fairness, and explainability. For each aspect, we categorize existing works into various categories, give general frameworks in each category, and more.
- HuggingFace Has Launched a Free Deep Reinforcement Learning Course - May 17, 2022.
Hugging Face has released a free course on Deep RL. It is self-paced and shares a lot of pointers on theory, tutorials, and hands-on guides.
- Popular Machine Learning Algorithms - May 16, 2022.
This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.
- Reinforcement Learning for Newbies - May 16, 2022.
A simple guide to reinforcement learning for a complete beginner. The blog includes definitions with examples, real-life applications, key concepts, and various types of learning resources.
- Centroid Initialization Methods for k-means Clustering - May 13, 2022.
This article is the first in a series of articles looking at the different aspects of k-means clustering, beginning with a discussion on centroid initialization.
- The “Hello World” of Tensorflow - May 13, 2022.
In this article, we will build a beginner-friendly machine learning model using TensorFlow.
- Deep Learning For Compliance Checks: What’s New? - May 12, 2022.
By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands.
- 5 Free Hosting Platform For Machine Learning Applications - May 12, 2022.
Learn about the free and easy-to-deploy hosting platform for your machine learning projects.
- Machine Learning’s Sweet Spot: Pure Approaches in NLP and Document Analysis - May 10, 2022.
While it is true that Machine Learning today isn’t ready for prime time in many business cases that revolve around Document Analysis, there are indeed scenarios where a pure ML approach can be considered.
- Machine Learning Key Terms, Explained - May 9, 2022.
Read this overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.
- Everything You Need to Know About Tensors - May 6, 2022.
In this article, we will cover the basics of the tensors.