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Tools & Languages
Scikit Optimize: Bayesian Hyperparameter Optimization in Python

Scikit Optimize: Bayesian Hyperparameter Optimization in Python

PythonTools & LanguagesPythonscikit optimizeposted by Jakub Czakon January 23, 2020

So you want to optimize hyperparameters of your machine learning model and you are thinking whether Scikit Optimize is the right tool for you? You are in the right place. In this article I will: show you an example of using skopt on a real problem, evaluate this library based on... Read more
DevOps to DevSecOps: All about the Journey!

DevOps to DevSecOps: All about the Journey!

Business + ManagementDataOpsTools & Languagesdevopsdevsecopsposted by ODSC Community January 20, 2020

What do DevOps and DevSecOps—two unions of various divisions the same organization, who has any intention of rushing to the aid of agility and faster innovation—have in common with each other and what are the relationships between these development tools? Are they indicating any trend which is going to... Read more
Introduction to Spark NLP: Foundations and Basic Components

Introduction to Spark NLP: Foundations and Basic Components

ModelingNLP/Text AnalyticsTools & LanguagesEast 2020Spark NLPposted by ODSC Community January 9, 2020

* This is the first article in a series of blog posts to help Data Scientists and NLP practitioners learn the basics of Spark NLP library from scratch and easily integrate it into their workflows. During this series, we will do our best to produce high-quality content and clear instructions with... Read more
15+ Resources to Get Started with R

15+ Resources to Get Started with R

Career InsightsFeatured PostRTools & Languagesposted by Elizabeth Wallace, ODSC January 3, 2020

R is the second most sought after language in data science behind Python, so gaining mastery of R is a prerequisite to a thriving career in the field. Whether you’re an experienced developer or a newbie considering a career move, here are some excellent resources so you can get... Read more
Quantifying R Package Dependency Risk

Quantifying R Package Dependency Risk

RTools & Languagesdependency riskposted by John Mount December 26, 2019

We recently commented on excess package dependencies as representing risk in the R package ecosystem. The question remains: how much risk? Is low dependency a mere talisman, or is there evidence it is a good practice (or at least correlates with other good practices)? [Related Article: Data-Driven Exploration of the R User Community... Read more
Machine Learning 101: Predicting Drug Use Using Logistic Regression In R

Machine Learning 101: Predicting Drug Use Using Logistic Regression In R

Machine LearningModelingRTools & LanguagesLogistic Regressionposted by Leihua Ye December 24, 2019

Executive Summary Generalized Linear Models (GLM) Three types of link function: Logit, Probit, and Complementary log-log (cloglog) Building a logistic regression to predict drug use and compare these three types of GLM In Machine Learning 101 courses, stats professors introduce GLM right after linear regression as the next stepping... Read more
XGBoost: Enhancement Over Gradient Boosting Machines

XGBoost: Enhancement Over Gradient Boosting Machines

Machine LearningModelingTools & LanguagesGradient BoostingXGBoostposted by Jacey Heuer December 16, 2019

In the first part of this discussion on XGBoost, I set the foundation for understanding the basic components of boosting. In brief, boosting uses sequences of decision trees that seek to reduce the residuals of the prior tree. In other words, each new tree uses the residual of the... Read more
Image Compression In 10 Lines of R Code

Image Compression In 10 Lines of R Code

ModelingRTools & Languagesimage compressingposted by Leihua Ye December 9, 2019

Principal Component Analysis (PCA) is a powerful Machine Learning tool. As an unsupervised learning technique, it excels in dimension reduction and feature extraction However, do you know we can use PCA to compress images? In this post, I’ll walk through the process and explain how PCA can compress images... Read more
A Quick Look Into Bootstrapping

A Quick Look Into Bootstrapping

Machine LearningModelingRStatisticsTools & LanguagesbootstrappingStatisticsposted by Leihua Ye December 3, 2019

Executive Summary As a resampling method, bootstrapping allows us to generate statistical inferences about the population from a single sample. Learn to bootstrap in R. Bootstrapping lies the foundation for several machine learning methods (e.g., Bagging. I’ll explain Bagging in a follow-up post). [Related Article: Discovering 135 Nights of... Read more
Implementing a Kernel Principal Component Analysis in Python

Implementing a Kernel Principal Component Analysis in Python

ModelingPythonTools & Languagesprincipal component analysisposted by ODSC Community November 26, 2019

In this article, we discuss implementing a kernel Principal Component Analysis in Python, with a few examples.  Many machine learning algorithms make assumptions about the linear separability of the input data. The perceptron even requires perfectly linearly separable training data to converge. Other algorithms that we have covered so... Read more
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