- What is Clustering and How Does it Work? - Oct 14, 2021.
Let us examine how clusters with different properties are produced by different clustering algorithms. In particular, we give an overview of three clustering methods: k-Means clustering, hierarchical clustering, and DBSCAN.
- Mastering Clustering with a Segmentation Problem - Aug 3, 2021.
The one stop shop for implementing the most widely used models in Python for unsupervised clustering.
- Unsupervised Learning for Predictive Maintenance using Auto-Encoders - Jan 14, 2021.
This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; Approaches for machine diagnosis; Machine diagnosis using machine learning
- How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.
- An easy guide to choose the right Machine Learning algorithm - May 21, 2020.
There's no free lunch in machine learning. So, determining which algorithm to use depends on many factors from the type of problem at hand to the type of output you are looking for. This guide offers several considerations to review when exploring the right ML approach for your dataset.
- Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.
- Anomaly Detection, A Key Task for AI and Machine Learning, Explained - Oct 21, 2019.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.
- KDnuggets™ News 19:n35, Sep 18: Which Data Science Skills are core and which are hot/emerging ones?; There is No Free Lunch in Data Science Features - Sep 18, 2019.
Check the results of KDnuggets' latest poll to find out which data science skills are core and which are hot/emerging ones; why is there no free lunch in data science?; training Scikit-learn 100x faster; poking fun at unsupervised machine learning; exploring the case for ensemble learning. All this and much more this week on KDnuggets.
- Cartoon: Unsupervised Machine Learning? - Sep 14, 2019.
New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?"
- K-means Clustering with Dask: Image Filters for Cat Pictures - Jun 18, 2019.
How to recreate an original cat image with least possible colors. An interesting use case of Unsupervised Machine Learning with K Means Clustering in Python.
- Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
- 10 Exciting Ideas of 2018 in NLP - Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
- Top /r/MachineLearning posts, August 2018: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents for sharing enormous datasets - Sep 15, 2018.
A range of interesting posts from the /r/MachineLearning Reddit group for the month of August, including: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents; Getting Alexa to respond to sign language using TensorFlow; PyCharm IDE.
- Machine Learning Cheat Sheets - Sep 11, 2018.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
- Unsupervised Learning Demystified - Aug 13, 2018.
Unsupervised learning is a pattern-finding technique for mining inspiration from your data. Let's demystify!
- Supervised vs. Unsupervised Learning - Apr 4, 2018.
Understanding the differences between the two main types of machine learning methods.
- 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.
- Key Takeaways from AI Conference in San Francisco 2017 – Day 1 - Sep 29, 2017.
Highlights and key takeaways from day 1 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.
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- Making Sense of Machine Learning - Jun 21, 2017.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
- 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.
- 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.
- Automatically Segmenting Data With Clustering - Feb 9, 2017.
In this post, we’ll walk through one such algorithm called K-Means Clustering, how to measure its efficacy, and how to choose the sets of segments you generate.
- 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.
- MDL Clustering: Unsupervised Attribute Ranking, Discretization, and Clustering - Aug 26, 2016.
MDL Clustering is a free software suite for unsupervised attribute ranking, discretization, and clustering based on the Minimum Description Length principle and built on the Weka Data Mining platform.
- 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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- Should We Be Rethinking Unsupervised Learning? - Aug 10, 2016.
Roland Memisevic, Assistant Professor at the University of Montreal and Chief Scientist at Twenty Billion Neurons, explores ideas on rethinking unsupervised learning, which he feels may explain what scientists have been doing wrong.
- America’s Next Topic Model - Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
- Top KDnuggets tweets, Sep 08-14: Dilbert brilliant take on Character; Free edX course: Data Science and Machine Learning - Sep 15, 2015.
Dilbert brilliant take on "Character is how you act when no one is watching"; Free edX Course: #DataScience & #MachineLearning, starts Sep 24; A New Approach for #DeepLearning Training in Unsupervised Learning; New Book: Fundamentals of Machine Learning for Predictive Data Analytics.
- Why unsupervised learning is more robust to adversarial distortions - Jan 30, 2015.
Yoshua Bengio, a leading expert on Deep Learning, explains why good unsupervised learning should be much more robust to adversarial distortions than supervised learning.