# Tag: SVM

**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.**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.**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.**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.