- Difference between distributed learning versus federated learning algorithms - Nov 19, 2021.
Want to know the difference between distributed and federated learning? Read this article to find out.
Algorithms, Distributed Systems, Federated Learning
Machine Learning Model Development and Model Operations: Principles and Practices - Oct 27, 2021.
The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.
Algorithms, Deployment, Feature Engineering, Machine Learning, MLOps
- How our Obsession with Algorithms Broke Computer Vision: And how Synthetic Computer Vision can fix it - Oct 15, 2021.
Deep Learning radically improved Machine Learning as a whole. The Data-Centric revolution is about to do the same. In this post, we’ll take a look at the pitfalls of mainstream Computer Vision (CV) and discuss why Synthetic Computer Vision (SCV) is the future.
Algorithms, Computer Vision, Synthetic Data
- WHT: A Simpler Version of the fast Fourier Transform (FFT) you should know - Jul 21, 2021.
The fast Walsh Hadamard transform is a simple and useful algorithm for machine learning that was popular in the 1960s and early 1970s. This useful approach should be more widely appreciated and applied for its efficiency.
Algorithms, Statistics, Time Series
- Ethics, Fairness, and Bias in AI - Jun 30, 2021.
As more AI-enhanced applications seep into our daily lives and expand their reach to larger swaths of populations around the world, we must clearly understand the vulnerabilities trained machine leaning models can exhibit based on the data used during development. Such issues can negatively impact select groups of people, so addressing the ethical decisions made by AI--possibly unknowingly--is important to the long-term fairness and success of this new technology.
AI, Algorithms, Bias, Ethics
- Ensemble Methods Explained in Plain English: Bagging - May 10, 2021.
Understand the intuition behind bagging with examples in Python.
Algorithms, Bagging, Ensemble Methods, Python
- XGBoost Explained: DIY XGBoost Library in Less Than 200 Lines of Python - May 3, 2021.
Understand how XGBoost work with a simple 200 lines codes that implement gradient boosting for decision trees.
Algorithms, Machine Learning, Python, XGBoost
- Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1 - Apr 22, 2021.
New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.
Algorithms, Bagging, Data Science, Data Scientist, Decision Trees, Linear Regression, Machine Learning, SVM, Top 10
- Beautiful decision tree visualizations with dtreeviz - Mar 8, 2021.
Improve the old way of plotting the decision trees and never go back!
Algorithms, Data Visualization, Decision Trees, Python
- Machine Learning – it’s all about assumptions - Feb 11, 2021.
Just as with most things in life, assumptions can directly lead to success or failure. Similarly in machine learning, appreciating the assumed logic behind machine learning techniques will guide you toward applying the best tool for the data.
Algorithms, Decision Trees, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, SVM, XGBoost
- My machine learning model does not learn. What should I do? - Feb 10, 2021.
This article presents 7 hints on how to get out of the quicksand.
Algorithms, Business Context, Data Quality, Hyperparameter, Machine Learning, Modeling, Tips
K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines - Jan 15, 2021.
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
Algorithms, K-means, Machine Learning, scikit-learn
All Machine Learning Algorithms You Should Know in 2021 - Jan 4, 2021.
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
Algorithms, Decision Trees, Explained, Gradient Boosting, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM
Key Data Science Algorithms Explained: From k-means to k-medoids clustering - Dec 29, 2020.
As a core method in the Data Scientist's toolbox, k-means clustering is valuable but can be limited based on the structure of the data. Can expanded methods like PAM (partitioning around medoids), CLARA, and CLARANS provide better solutions, and what is the future of these algorithms?
Algorithms, Clustering, Explained, K-means
- XGBoost: What it is, and when to use it - Dec 23, 2020.
XGBoost is a tree based ensemble machine learning algorithm which is a scalable machine learning system for tree boosting. Read more for an overview of the parameters that make it work, and when you would use the algorithm.
Algorithms, Ensemble Methods, XGBoost
- Navigate the road to Responsible AI - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
AI, Algorithms, Ethics, Responsible AI
- Implementing the AdaBoost Algorithm From Scratch - Dec 10, 2020.
AdaBoost technique follows a decision tree model with a depth equal to one. AdaBoost is nothing but the forest of stumps rather than trees. AdaBoost works by putting more weight on difficult to classify instances and less on those already handled well. AdaBoost algorithm is developed to solve both classification and regression problem. Learn to build the algorithm from scratch here.
Adaboost, Algorithms, Ensemble Methods, Machine Learning, Python
- How to Know if a Neural Network is Right for Your Machine Learning Initiative - Nov 26, 2020.
It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.
Algorithms, Machine Learning, Neural Networks
- Know-How to Learn Machine Learning Algorithms Effectively - Nov 23, 2020.
The takeaway from the story is that machine learning is way beyond a simple fit and predict methods. The author shares their approach to actually learning these algorithms beyond the surface.
Algorithms, Complexity, Interpretability, Machine Learning
How to Acquire the Most Wanted Data Science Skills - Nov 13, 2020.
We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.
Algorithms, Amazon, Apache Spark, AWS, Computer Vision, Data Science, Data Science Skills, Deep Learning, Docker, NLP, NoSQL, PyTorch, Reinforcement Learning, TensorFlow
- Doing the impossible? Machine learning with less than one example - Nov 9, 2020.
Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
Algorithms, K-nearest neighbors, Machine Learning, Research
How to Explain Key Machine Learning Algorithms at an Interview - Oct 19, 2020.
While preparing for interviews in Data Science, it is essential to clearly understand a range of machine learning models -- with a concise explanation for each at the ready. Here, we summarize various machine learning models by highlighting the main points to help you communicate complex models.
Algorithms, Decision Trees, Interview Questions, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM
- Exploring The Brute Force K-Nearest Neighbors Algorithm - Oct 12, 2020.
This article discusses a simple approach to increasing the accuracy of k-nearest neighbors models in a particular subset of cases.
Algorithms, K-nearest neighbors, Machine Learning, Python
The List of Top 10 Lists in Data Science - Aug 14, 2020.
The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.
Algorithms, Data Science, Data Science Skills, Datasets, Influencers, LinkedIn, Python, Top 10
- Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook - Jul 21, 2020.
The second edition of Data Mining and Machine Learning: Fundamental Concepts and Algorithms is available to read freely online, and includes a new part on regression with chapters on linear regression, logistic regression, neural networks, deep learning and regression assessment.
Algorithms, Data Mining, Free ebook, Machine Learning
- Time Complexity: How to measure the efficiency of algorithms - Jun 24, 2020.
When we consider the complexity of an algorithm, we shouldn’t really care about the exact number of operations that are performed; instead, we should care about how the number of operations relates to the problem size.
Algorithms, Complexity, Programming
Understanding Machine Learning: The Free eBook - Jun 15, 2020.
Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.
Algorithms, Book, Free ebook, Machine Learning
Python For Everybody: The Free eBook - May 25, 2020.
Get back to fundamentals with this free eBook, Python For Everybody, approaching the learning of programming from a data analysis perspective.
Algorithms, Free ebook, Programming, Python
- Visualizing Decision Trees with Python (Scikit-learn, Graphviz, Matplotlib) - Apr 15, 2020.
Learn about how to visualize decision trees using matplotlib and Graphviz.
Algorithms, Decision Trees, Matplotlib, Python, Visualization
- Introduction to the K-nearest Neighbour Algorithm Using Examples - Apr 1, 2020.
Read this concise summary of KNN, a supervised and pattern classification learning algorithm which helps us find which class the new input belongs to when k nearest neighbours are chosen and distance is calculated between them.
Algorithms, K-nearest neighbors, Machine Learning, Python, scikit-learn
- Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU? - Mar 26, 2020.
Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.
Algorithms, Deep Learning, GPU, Machine Learning
- Making sense of ensemble learning techniques - Mar 26, 2020.
This article breaks down ensemble learning and how it can be used for problem solving.
Algorithms, Data Science, Ensemble Methods, Machine Learning
A Top Machine Learning Algorithm Explained: Support Vector Machines (SVM) - Mar 18, 2020.
Support Vector Machines (SVMs) are powerful for solving regression and classification problems. You should have this approach in your machine learning arsenal, and this article provides all the mathematics you need to know -- it's not as hard you might think.
Algorithms, Explained, Linear Algebra, Machine Learning, Support Vector Machines, SVM
- How to Get Started With Algorithmic Finance - Jan 23, 2020.
Algorithmic finance has been around for decades as a money-making tool, and it's not magic. Learn about some practical strategies along with and introduction to code you can use to get started.
Algorithms, Finance, Hedge fund, Investment, Time Series
- Random Forest® — A Powerful Ensemble Learning Algorithm - Jan 22, 2020.
The article explains the Random Forest algorithm and how to build and optimize a Random Forest classifier.
Algorithms, Ensemble Methods, Python, random forests algorithm
Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem - Jan 20, 2020.
The new algorithm takes a novel approach to neural architecture search.
Algorithms, Microsoft, Neural Networks
- Handling Trees in Data Science Algorithmic Interview - Jan 16, 2020.
This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.
Algorithms, Data Science, Decision Trees, Interview Questions
- Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
Which algorithm works best for unbalanced data? Are there any tradeoffs?
Algorithms, Classification, Machine Learning, R, ROC-AUC, Unbalanced
- 5 Ways to Apply Ethics to AI - Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker.
Algorithms, Bias, Ethics, Goodhart’s Law, Machine Learning, Social Good
- The 5 Sampling Algorithms every Data Scientist need to know - Sep 18, 2019.
Algorithms are at the core of data science and sampling is a critical technical that can make or break a project. Learn more about the most common sampling techniques used, so you can select the best approach while working with your data.
Algorithms, Sampling
There is No Free Lunch in Data Science - Sep 12, 2019.
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.
Algorithms, Data Science, Machine Learning, Optimization
- A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way.
Algorithms, Explained, Machine Learning, Support Vector Machines, SVM
- The 5 Graph Algorithms That Data Scientists Should Know - Sep 10, 2019.
In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
Algorithms, Data Science, Data Scientist, Graph, Python
- How to count Big Data: Probabilistic data structures and algorithms - Aug 26, 2019.
Learn how probabilistic data structures and algorithms can be used for cardinality estimation in Big Data streams.
Algorithms, Big Data, Probability
- Automate Stacking In Python: How to Boost Your Performance While Saving Time - Aug 21, 2019.
Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it.
Algorithms, Big Data, Data Science, Python
- Coding Random Forests® in 100 lines of code* - Aug 7, 2019.
There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks.
Algorithms, Machine Learning, Multicollinearity, R, random forests algorithm
- An Overview of Outlier Detection Methods from PyOD – Part 1 - Jun 27, 2019.
PyOD is an outlier detection package developed with a comprehensive API to support multiple techniques. This post will showcase Part 1 of an overview of techniques that can be used to analyze anomalies in data.
Algorithms, Big Data, Outliers, Python
- 10 Gradient Descent Optimisation Algorithms + Cheat Sheet - Jun 26, 2019.
Gradient descent is an optimization algorithm used for minimizing the cost function in various ML algorithms. Here are some common gradient descent optimisation algorithms used in the popular deep learning frameworks such as TensorFlow and Keras.
Algorithms, Deep Learning, Gradient Descent, Optimization
The Machine Learning Puzzle, Explained - Jun 17, 2019.
Lots of moving parts go into creating a machine learning model. Let's take a look at some of these core concepts and see how the machine learning puzzle comes together.
Algorithms, Explained, Machine Learning, Modeling
- Think Like an Amateur, Do As an Expert: Lessons from a Career in Computer Vision - May 17, 2019.
Dr. Takeo Kanade shared his life lessons from an illustrious 50-year career in Computer Vision at last year's Embedded Vision Summit. You have a chance to attend the 2019 Embedded Vision Summit, from May 20-23, in the Santa Clara Convention Center, Santa Clara CA.
AI, Algorithms, Computer Vision, Deep Learning, Machine Learning
- Naive Bayes: A Baseline Model for Machine Learning Classification Performance - May 7, 2019.
We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.
Pages: 1 2
Algorithms, Data Science, Machine Learning, Naive Bayes, Python, scikit-learn, Statistics
Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.
Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression
- How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms - Apr 16, 2019.
We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example.
Algorithms, Clustering, Explained
- Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
Algorithms, Data Science, Machine Learning, Poll
Artificial Neural Networks Optimization using Genetic Algorithm with Python - Mar 18, 2019.
This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.
Pages: 1 2
AI, Algorithms, Deep Learning, Machine Learning, Neural Networks, numpy, Optimization, Python
- Designing Ethical Algorithms - Mar 8, 2019.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
AI, Algorithms, Bias, Ethics, Machine Learning
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
Algorithms, Bias, Machine Learning
A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn
10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
AI, Algorithms, Big Data, Data Science, Deep Learning, Machine Learning, MIT, NLP, Reinforcement Learning, U. of Washington, UC Berkeley, Yandex
- A Concise Explanation of Learning Algorithms with the Mitchell Paradigm - Oct 5, 2018.
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.
Algorithms, Learning, Machine Learning, Tom Mitchell
- Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
Algorithms, Linear Regression, Python
- Selecting the Best Machine Learning Algorithm for Your Regression Problem - Aug 1, 2018.
This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!
Algorithms, Machine Learning, Regression
Genetic Algorithm Implementation in Python - Jul 24, 2018.
This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.
Algorithms, Genetic Algorithm, Python
- Clustering Using K-means Algorithm - Jul 18, 2018.
This article explains K-means algorithm in an easy way. I’d like to start with an example to understand the objective of this powerful technique in machine learning before getting into the algorithm, which is quite simple.
Algorithms, Clustering, K-means
Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
Algorithms, Data Science, Machine Learning, Online Education, Statistics
Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
Pages: 1 2
Algorithms, Clustering, Convolutional Neural Networks, Decision Trees, Machine Learning, Neural Networks, PCA, Regression, SVM
- Top 20 Deep Learning Papers, 2018 Edition - Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
Algorithms, Deep Learning, Machine Learning, Neural Networks, TensorFlow, Text Analytics, Trends
- Multiscale Methods and Machine Learning - Mar 19, 2018.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
Algorithms, Data Science, Deep Learning, Machine Learning, Statistics
- 5 Things You Need To Know About Data Science - Feb 19, 2018.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
Algorithms, BI, Data Analytics, Data Mining, Data Science, Data Science Education, Data Scientist, Google Trends, Jobs, Machine Learning
Logistic Regression: A Concise Technical Overview - Feb 16, 2018.
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
Algorithms, Classification, Logistic Regression, Machine Learning, Regression
- A Basic Recipe for Machine Learning - Feb 13, 2018.
One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.
Algorithms, Andrew Ng, Coursera, Deep Learning, deeplearning.ai
- Which Machine Learning Algorithm be used in year 2118? - Feb 9, 2018.
So what were the answers popping in your head ? Random forest, SVM, K means, Knn or even Deep Learning? No, for the answer, we turn to Lindy Effect.
Algorithms, Machine Learning, Regression, Trends
- Topological Data Analysis for Data Professionals: Beyond Ayasdi - Jan 16, 2018.
We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.
Algorithms, Clustering, R, Regression, Topological Data Analysis
- How to Improve Machine Learning Algorithms? Lessons from Andrew Ng, part 2 - Dec 21, 2017.
The second chapter of ML lessons from Ng’s experience. This one will only be talking about Human Level Performance & Avoidable Bias.
Algorithms, Andrew Ng, Bias, Machine Learning
- Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation - Dec 18, 2017.
If you are trying to make your algorithms run faster, you may want to consider reviewing some important points on design and implementation.
ActiveState, Algorithms, Implementation, Python
- New Poll: Which Data Science / Machine Learning methods and tools you used? - Nov 20, 2017.
Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.
Algorithms, Data Science Tools, Machine Learning, Poll
The 10 Statistical Techniques Data Scientists Need to Master - Nov 15, 2017.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
Pages: 1 2
Algorithms, Data Science, Data Scientist, Machine Learning, Statistical Learning, Statistics
Machine Learning Algorithms: Which One to Choose for Your Problem - Nov 14, 2017.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
Algorithms, Machine Learning, Reinforcement Learning, Statsbot, Supervised Learning, Unsupervised Learning
- Density Based Spatial Clustering of Applications with Noise (DBSCAN) - Oct 26, 2017.
DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.
Algorithms, Clustering, DBSCAN, Machine Learning
- Top 10 Machine Learning with R Videos - Oct 24, 2017.
A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.
Algorithms, Clustering, K-nearest neighbors, Machine Learning, PCA, R, Text Mining, Top 10, Youtube
Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
Pages: 1 2
Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10
Random Forests®, Explained - Oct 17, 2017.
Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
Algorithms, CART, Decision Trees, Ensemble Methods, Explained, Machine Learning, random forests algorithm, Salford Systems
- XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
Algorithms, Data Science, Explained, Kaggle, Machine Learning
Understanding Machine Learning Algorithms - Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
Algorithms, Ensemble Methods, Gradient Boosting, Machine Learning, Neural Networks, Predictive Analytics, random forests algorithm, SVM
- K-Nearest Neighbors – the Laziest Machine Learning Technique - Sep 12, 2017.
K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.
Algorithms, K-nearest neighbors, Machine Learning, RapidMiner
- Search Millions of Documents for Thousands of Keywords in a Flash - Sep 1, 2017.
We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.
Algorithms, Data Science, GitHub, NLP, Python, Search, Search Engine, Text Mining
- Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples - Aug 28, 2017.
In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.
Pages: 1 2 3
Algorithms, Machine Learning, Statsbot, Support Vector Machines, SVM
- How To Write Better SQL Queries: The Definitive Guide – Part 2 - Aug 24, 2017.
Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.
Pages: 1 2
Algorithms, Complexity, Databases, Relational Databases, SQL
- Recommendation System Algorithms: An Overview - Aug 22, 2017.
This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.
Algorithms, Recommendations, Recommender Systems, Statsbot
Machine Learning Algorithms: A Concise Technical Overview – Part 1 - Aug 4, 2017.
These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.
Algorithms, Machine Learning
- The Machine Learning Abstracts: Decision Trees - Aug 3, 2017.
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
Algorithms, Decision Trees, Machine Learning
- The Machine Learning Abstracts: Classification - Jul 27, 2017.
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
Algorithms, Classification, Machine Learning
The Machine Learning Algorithms Used in Self-Driving Cars - Jun 19, 2017.
Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.
Algorithms, Boosting, Machine Learning, Self-Driving Car
Which Machine Learning Algorithm Should I Use? - Jun 1, 2017.
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.
Algorithms, Cheat Sheet, Machine Learning, Reinforcement Learning, Supervised Learning, Unsupervised Learning
Keep it simple! How to understand Gradient Descent algorithm - Apr 28, 2017.
In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.
Algorithms, Gradient Descent
- What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
Check this out: A topic wise collection of 100+ data science interview questions from top companies.
Algorithms, Data Science, Google, Hadoop, Interview Questions, Machine Learning, Microsoft, Statistics, Uber
Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
Algorithms, Dataiku, Decision Trees, Gradient Boosting, Linear Regression, Logistic Regression, random forests algorithm
- Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method - Mar 13, 2017.
What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.
Algorithms, Clustering, Dataset, K-means
- 17 More Must-Know Data Science Interview Questions and Answers, Part 2 - Feb 22, 2017.
The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.
Algorithms, Data Science, Ensemble Methods, Feature Engineering, Feature Selection, High-dimensional, Interview Questions, Overfitting, Unsupervised Learning
- Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
Algorithms, Machine Learning, Programming, Python
- More Data or Better Algorithms: The Sweet Spot - Jan 17, 2017.
We examine the sweet spot for data-driven Machine Learning companies, where is not too easy and not too hard to collect the needed data.
Algorithms, Big Data, Data, Datasets, Machine Learning
- Random Forests® in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
Algorithms, Classification, Ensemble Methods, Python, random forests algorithm, Yhat
- Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview - Nov 24, 2016.
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
Algorithms, Linear Regression
- Predictive Science vs Data Science - Nov 22, 2016.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
Algorithms, Applied Statistics, Data Science, Prediction
- Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications - Nov 10, 2016.
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
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Algorithms, CUDA, GPU, NVIDIA, Parallelism
- Decision Tree Classifiers: A Concise Technical Overview - Nov 3, 2016.
The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
Algorithms, C4.5, CART, Decision Trees
- Frequent Pattern Mining and the Apriori Algorithm: A Concise Technical Overview - Oct 27, 2016.
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
Algorithms, Apriori, Association Rules, Frequent Pattern Mining
- Comparing Clustering Techniques: A Concise Technical Overview - Sep 26, 2016.
A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.
Algorithms, Clustering, K-means, Machine Learning
- Data Science Basics: 3 Insights for Beginners - Sep 22, 2016.
For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.
Algorithms, Beginners, Datasets, Overfitting, Supervised Learning, Unsupervised Learning
- Support Vector Machines: A Concise Technical Overview - Sep 21, 2016.
Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality.
Algorithms, Machine Learning, Support Vector Machines
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization
Top Algorithms and Methods Used by Data Scientists - Sep 12, 2016.
Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.
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Algorithms, Clustering, Data Visualization, Decision Trees, Poll, Regression
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction
- A Gentle Introduction to Bloom Filter - Aug 24, 2016.
The Bloom Filter is a probabilistic data structure which can make a tradeoff between space and false positive rate. Read more, and see an implementation from scratch, in this post.
Algorithms, Efficiency, Python
- The 10 Algorithms Machine Learning Engineers Need to Know - Aug 18, 2016.
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.
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Algorithms, Machine Learning, Supervised Learning, Unsupervised Learning
- Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy - Aug 12, 2016.
Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.
Algorithms, R, Statistics
- Contest 2nd Place: Automating Data Science - Aug 3, 2016.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
Algorithms, Automated, Automated Data Science, Feature Selection, Machine Learning
- 10 Algorithm Categories for AI, Big Data, and Data Science - Jul 14, 2016.
With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
AI, Algorithms, Big Data, Data Science
- Improving Nudity Detection and NSFW Image Recognition - Jun 25, 2016.
This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?
Algorithmia, Algorithms, Classification
- Machine Learning Trends and the Future of Artificial Intelligence - Jun 22, 2016.
The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.
Algorithmia, Algorithms, Artificial Intelligence, Cloud, Machine Intelligence, Machine Learning
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
Algorithms, Backpropagation, Explanation, Machine Learning, Neural Networks
- Data Science of Variable Selection: A Review - Jun 7, 2016.
There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.
Algorithms, Big Data, Feature Selection, Statistics
- Why Implement Machine Learning Algorithms From Scratch? - May 6, 2016.
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
Algorithms, Machine Learning
- Datasets Over Algorithms - May 3, 2016.
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
Algorithms, Datasets
- Basics of GPU Computing for Data Scientists - Apr 7, 2016.
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
Algorithms, CUDA, Data Science, GPU, NVIDIA
- The Art of Data Science: The Skills You Need and How to Get Them - Dec 28, 2015.
Learn, how to turn the deluge of data into the gold by algorithms, feature engineering, reasoning out business value and ultimately building a data driven organization.
Algorithms, Data Science Skills, Feature Engineering, MapR
- The Master Algorithm – new book by top Machine Learning researcher Pedro Domingos - Sep 25, 2015.
Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.
Algorithms, Book, Machine Learning, Pedro Domingos
- TheWalnut.io: An Easy Way to Create Algorithm Visualizations - Jul 29, 2015.
Google's DeepDream project has gone viral which allows to visualize the deep learning neural networks. It highlights a need for a generalized algorithm visualization tool, in this post we introduce to you one such effort.
Algorithms, Data Visualization, Javascript, Python
- Top 10 Data Mining Algorithms, Explained - May 21, 2015.
Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.
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Algorithms, Apriori, Bayesian, Boosting, C4.5, CART, Data Mining, Explained, K-means, K-nearest neighbors, Naive Bayes, Page Rank, Support Vector Machines, Top 10
- Sibyl: Google’s system for Large Scale Machine Learning - Aug 20, 2014.
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
Algorithms, Boosting, Google, Machine Learning, Sibyl
