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1. Machine learning jupyter notebook tutorial how to#
2. Machine learning jupyter notebook tutorial install#

With native support for Jupyter notebooks combined with Anaconda, it's easy to get started. Visual Studio Code and the Python extension provide a great editor for data science scenarios.

Machine learning jupyter notebook tutorial install#

If you go this route, you will need to install the following packages: pandas, jupyter, seaborn, scikit-learn, keras, and tensorflow. Alternatively, if you'd prefer not to use Anaconda or Miniconda, you can create a Python virtual environment and install the packages needed for the tutorial using pip. Note: If you already have the full Anaconda distribution installed, you don't need to install Miniconda. Both extensions are published by Microsoft. For more details on installing extensions, see Extension Marketplace. By default, the Python extension installs the Jupyter extension for you. The Python extension for VS Code and Jupyter extension for VS Code from the Visual Studio Marketplace. Make sure to install them if you haven't already. The following installations are required for the completion of this tutorial.

Machine learning jupyter notebook tutorial how to#

Specifically, using passenger data from the Titanic, you will learn how to set up a data science environment, import and clean data, create a machine learning model for predicting survival on the Titanic, and evaluate the accuracy of the generated model. This tutorial demonstrates using Visual Studio Code and the Microsoft Python extension with common data science libraries to explore a basic data science scenario.