Learn how to convert strings to datetime objects in Python and why doing so has become standard practice for working data scientists today. Visualize the training parameters, metrics, hyperparameters or any statistics of your neural network with TensorBoard!
This tutorial will demonstrate how you can install Anaconda, a powerful package manager, on your Mac. Learn how to create custom export templates for your Jupyter Notebooks using Jinja2. Learn about Python sets: what they are, how to create them, when to use them, built-in functions, and their relationship to set theory operations. 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. Learn the common tricks to handle categorical data and preprocess it to build machine learning models! This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself. Learn about Random Forests and build your own model in Python, for both classification and regression. Importing data is the first step in any data science project. Learn why today's data scientists prefer 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()! Discover Long Short-Term Memory (LSTM) networks in Python and how you can use them to make stock market predictions! Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python! In this tutorial, you'll learn about the statistical concepts behind survival analysis and you'll implement a real-world application of these methods in R.