Machine Learning Tutorial

A comprehensive guide to building, visualizing, and interpreting decision tree models with R.

Visualize the training parameters, metrics, hyperparameters or any statistics of your neural network with TensorBoard!

This article covers how and when to use random forest classification with scikit-learn, focusing on concepts, workflow, and examples. We also cover how to use the confusion matrix and feature importances.

Learn what Generative Adversarial Networks are without going into the details of the math and code a simple GAN that can create digits!

In this tutorial, you'll learn about absolute and weighted word frequency in text mining and how to calculate it with defaultdict and pandas DataFrames.

Explore the key concepts in object detection and learn how they are implemented in SSD and Faster RCNN, which are available in the Tensorflow Detection API.

Learn what k-means is and discover why it’s one of the most used clustering algorithms in data science

Tackle feature selection in R: explore the Boruta algorithm, a wrapper built around the Random Forest classification algorithm, and its implementation!

Boost your machine learning results and discover ensembles in R with the SuperLearner package: learn about the Random Forest algorithm, bagging, and much more!

Discover active learning, a case of semi-supervised machine learning: from its definition and its benefits, to applications and modern research into it.

Dive into the lyrics of Prince's music with R: use text mining and Exploratory Data Analysis (EDA) to shed insight on The Artist's career.

In this tutorial, you’ll see what transfer learning is, what some of its applications are and why it is critical skill as a data scientist.