python

+1## Converting Strings to Dates as datetime Objects

Learn how to convert strings to datetime objects in Python and why doing so has become standard practice for working data scientists today.## TensorBoard Tutorial

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

## Installing Anaconda on Mac OS X

This tutorial will demonstrate how you can install Anaconda, a powerful package manager, on your Mac.jupyter

## Custom Templates for Jupyter Notebooks with Jinja2

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

## Python Sets and Set Theory

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.## How to Install and Use Homebrew

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.data manipulation

+1## Handling Categorical Data in Python

Learn the common tricks to handle categorical data and preprocess it to build machine learning models!## How to install R on Windows, Mac OS X and Ubuntu

This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself.python

## Understanding Random Forests Classifiers in Python

Learn about Random Forests and build your own model in Python, for both classification and regression.## Pandas Tutorial: Importing Data with read_csv()

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.deep learning

+2## Demystifying Generative Adversarial Nets (GANs)

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

+1## Long to Wide Data in R

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()!deep learning

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