Learn about the various options you have to setup a data science environment with Python, R, Git, and Unix Shell on your local computer. In this tutorial, you will learn and understand how to read jpeg format fingerprint images, reconstructing them using convolutional autoencoder. In this tutorial, you will build four models using Latent Dirichlet Allocation (LDA) and K-Means clustering machine learning algorithms. Learn how you can predict the status of a H-1B visa application with Machine Learning in Python. Learn all about decision trees, a form of supervised learning used in a variety of ways to solve regression and classification problems. Learn how Python can be used more effectively than Excel, with the Pandas package. 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. 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 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()!