- KDnuggets™ News 20:n12, Mar 25: 24 Best (and Free) Books To Understand Machine Learning; Coronavirus Daily Change and Poll Analysis; 9 lessons learned during 1st year as a Data Scientist - Mar 25, 2020.
Read our analysis of coronavirus data and poll results; Use your time indoors to learn with 24 best and free books to understand Machine Learning; Study the 9 important lessons from the first year as a Data Scientist; Understand the SVM, a top ML algorithm; check a comprehensive list of AI resources for online learning; and more.
- 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.
- A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way.
- Common Machine Learning Obstacles - Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.
- How can quantum computing be useful for Machine Learning - Apr 12, 2019.
We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.
- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
- Journey to Machine Learning – 100 Days of ML Code - Sep 7, 2018.
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
- 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.
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- The Value of Semi-Supervised Machine Learning - Jan 17, 2018.
This post shows you how to label hundreds of thousands of images in an afternoon. You can use the same approach whether you are labeling images or labeling traditional tabular data (e.g, identifying cyber security atacks or potential part failures).
- 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.
- 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.
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- How to squeeze the most from your training data - Jul 27, 2017.
In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.
- What is a Support Vector Machine, and Why Would I Use it? - Feb 23, 2017.
Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries.
- The Evolution of Classification, Oct 19, Oct 26 Webinars - Oct 7, 2016.
Join us for this two part webinar series on the Evolution of Classification, presented by Senior Scientist, Mikhail Golovnya.
- Data Mining History: The Invention of Support Vector Machines - Jul 4, 2016.
The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.
- Dealing with Unbalanced Classes, SVMs, Random Forests®, and Decision Trees in Python - Apr 29, 2016.
An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.
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- When Does Deep Learning Work Better Than SVMs or Random Forests®? - Apr 22, 2016.
Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.
- Does Deep Learning Come from the Devil? - Oct 9, 2015.
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
- Decision Boundaries for Deep Learning and other Machine Learning classifiers - Jun 15, 2015.
H2O, one of the leading deep learning framework in python, is now available in R. We will show how to get started with H2O, its working, plotting of decision boundaries and finally lessons learned during this series.
- Top 10 R Packages to be a Kaggle Champion - Apr 21, 2015.
Kaggle top ranker Xavier Conort shares insights on the “10 R Packages to Win Kaggle Competitions”.
- Machine Learning 201: Does Balancing Classes Improve Classifier Performance? - Apr 9, 2015.
The author investigates if balancing classes improves performance for logistic regression, SVM, and Random Forests, and finds where it helps the performance and where it does not.
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- 7 common mistakes when doing Machine Learning - Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
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- Top /r/MachineLearning posts, January - Feb 13, 2015.
Talking Machines, SVM lectures, a new Stanford statistical learning online course, and a listing of open-source datasets top the most popular Reddit posts on /r/MachineLearning for the month of January.
- Top /r/MachineLearning posts, Jan 11-17 - Jan 18, 2015.
SVMs, open source datasets, Bayesian decision theory, game AI, and deep learning visualizations are all featured in the past week's top /r/MachineLearning posts.
- Top KDnuggets tweets, Dec 7-14: Google new CAPTCHA trains #AI; Random Forests, SVM give best results - Dec 15, 2014.
Which one is the bunny? Google new CAPTCHA bot-trap trains #AI; O'Reilly Data Scientist Salary and Tools Survey 2014; Microsoft brings the power of #MachineLearning to Office Online; 10 Data Science Newsletters to Subscribe to.
- Top KDnuggets tweets, Nov 26-28: Facebook AI team hires Vladimir Vapnik, father of SVM - Nov 29, 2014.
Facebook's #AI team hires Vladimir Vapnik, father of popular #SVM algorithm; Starting data analysis/wrangling with R: Things I wish I'd been told; How to deal with missing values - advice from @Knime #DataMining; Understanding The Various Sources of #BigData - Infographic.
- Top KDnuggets tweets, Sep 26-28: Any data scientist worth their salary will say you should start with a question - Sep 29, 2014.
CNN embarrassing lack of "Data Quality" - this #Scotland Independence poll adds; Statistical & Machine learning with R; Any data scientist worth their salary will say you should start with a question; Automotive Customer Churn Prediction using SVM and SOM.