Data Science Tutorials

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.

Importing data is the first step in any data science project. Learn why today's data scientists prefer the pandas read_csv() function to do this.

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

Learn why you would transform your data from a long to a wide format and vice versa and explore how to do this in R with melt() and dcast()!

Explore graph-structured multi-task regression with the GFLASSO R package with this tutorial!

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.

Explore the definition of centrality, learn what different types of centrality measures exist in network analysis and pick the best one for a given network!

Start analyzing titanic data with R and the tidyverse: learn how to filter, arrange, summarise, mutate and visualize your data with dplyr and ggplot2!

Discover all about logistic regression: how it differs from linear regression, how to fit and evaluate these models it in R with the glm() function and more!

Discover the Python pickle module: learn about serialization, when (not) to use it, how to compress pickled objects, multiprocessing, and much more!

In this tutorial, you'll learn more about autoencoders and how to build convolutional and denoising autoencoders with the notMNIST dataset in Keras.