Data Science Tutorials

In this tutorial, you will build four models using Latent Dirichlet Allocation (LDA) and K-Means clustering machine learning algorithms.

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

Learn how Python can be used more effectively than Excel, with the Pandas package.

Learn how to convert strings to datetime objects in Python using strptime(), dateutil, and pandas with practical code examples.

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

A practical guide to installing Anaconda on a Mac with native ARM64 support, verifying the download, initializing conda, and setting up clean environments.

Learn how to create custom export templates for your Jupyter Notebooks using Jinja2.

Discover Homebrew for data science. Learn how you can use this package manager to install, update, and remove technologies such as Apache Spark and Graphviz.

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!

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 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()!