In this blog post, Jannik will show you how to deploy your machine learning models as a REST API and how to make requests to the API from within your Python code.
Model Regularization – The Bayesian Way
Are you confused by Bayesian statistics? If you understand Ridge regression, one of the most common Bayesian models is within your reach! This post gives a brief intro to Bayesian thinking and shows you just how similar Ridge regression is to Bayesian linear regression by walking you through the math and exploring how coefficient estimates from both models compare.
Machine Learning Goes Causal I: Why Causality Matters
A new field of Machine Learning is born: Causal Machine Learning. Learn here what it is and why it is crucial for the future of Data Science.
Tuning Random Forest on Time Series Data
Training random forests on time series is one thing, but tuning them? It’s not like you can just apply cross validation and be done with it. Or can you? This post forms part two our mini-series “Time Series Forecasting with Random Forest”. Find out how you can tune the hyperparameters of the random forest algorithm when dealing with time series data. The answers might surprise you!
Time series forecasting with random forest
This blog post looks at how we can improve predictive accuracy by combining forecasts from different models.
Machine Learning Interpretability @DataUniversity2019
Vom 09.-10. Oktober findet die Data University an der Goethe-Uni in Frankfurt statt, präsentiert von STATWORX & BARC.
Data Science Bootcamp
Du interessiertst Dich für Data Science und möchtest Deine Daten sinnvoll nutzen? Im Data Science Bootcamp von der STATWORX Data Academy bekommen Einsteiger den perfekten Start in die Praxis. Gemeinsam erarbeiten wir in 5 Tagen die wichtigsten Grundlagen von Data Science, Machine Learning und Programmierung. Du entwickelst R und Python Modelle und arbeitest wie ein echter Data Scientist. Was …
Coding Random Forests in 100 lines of code*
In our series of explaining method in 100 lines of code, we tackle random forest this time! We build it from scratch and explore it’s functions.
Coding Gradient boosted machines in 100 lines of R code
Motivation 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. At STATWORX we discuss algorithms daily to evaluate their usefulness for a specific project or problem. In any case, …
A Performance Benchmark of Different AutoML Frameworks
In a recent blog post our CEO Sebastian Heinz wrote about Google’s newest stroke of genius – AutoML Vision. A cloud service „that is able to build deep learning models for image recognition completely fully automated and from scratch„. AutoML Vision is part of the current trend towards the automation of machine learning tasks. This trend started with automation of …
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