Learn by doing

Python, Kubernetes, Docker, and more. Just open your browser and dive in.

Interactive learning is the fastest way to explore a new technology. Because you’re not just reading about it—you’re also manipulating it in real time to discover how it works. In the past, you had to set up complex environments and datasets to get your head around cloud-based infrastructure and orchestration. But with O’Reilly online learning, it’s as simple as opening a browser.

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Interactive scenarios

Learn how a new technology works within a real dev environment—complete with expert guidance to solve real problems when moving to microservices or developing cloud native apps. So you’re properly prepared when it’s time to face real-world situations that can affect your work.

  • Quickly train on modern technologies like Kubernetes, Docker, Python, and more.
  • No need to spend time setting up environments to start learning—it’s already right in your browser.
  • Our experts guide you through how each tool responds in specific scenarios you’ll likely face.
Katacoda scenario screenshot Technology logos

Here are a few examples of interactive scenarios you’ll find on O’Reilly.

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Python Cookbook: Classes & Objects

The primary focus of these scenarios is to present recipes to common programming patterns related to class definitions.

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Kubernetes Fundamentals: First Kubernetes Application

This scenario takes you through the basics of deploying an application on Kubernetes.

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Launch a Docker Container

In this first scenario, we’ll explore how you can start and connect to your first container using Docker.

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Build and Save Robust Machine Learning Models

In this first step, we’ll build and save a robust machine learning model with DataFrameMapper, pipeline, and pickle.

Interactive sandboxes

Jump right into real dev environments preconfigured for Python, Ubuntu, Kubernetes, Java, SQL, and more. So you can start exploring and experimenting with live code all on your own without spending time downloading and installing everything to set up your environment before you can even begin.

  • Develop skills at your own pace with unguided sandboxes.
  • Access environments instantly—nothing to download or install.
  • Experiment away! You can’t hurt anything. Sandboxes are a safe place to test for innovative solutions to hard problems.
Interactive sandbox screenshot

Here are a few examples of interactive sandboxes you’ll find on O’Reilly.

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Ubuntu Sandbox

Explore Ubuntu in a sandboxed environment.

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Git Sandbox

Explore Git in a sandboxed environment..

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Java Sandbox

Explore Java in a sandboxed environment.

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Ruby Sandbox

Explore Ruby in a sandboxed environment.

Interactivity powered by Jupyter

Getting into data, AI, and machine learning can be overwhelming—including for your sysadmins who have to get it set up. But with Jupyter, it’s like having some of the world’s foremost experts over the shoulders of your team while they learn to analyze and visualize vast amounts of data.

  • Get your teams proficient in Python and scikit-learn without using your company’s actual data as a training tool.
  • They’ll explore data hands-on, in a live coding environment, right in their browser. No configuration needed.
  • Renowned O’Reilly experts lead your team through principles while they edit and run code at each step to see results in action.
Jupyter editor screenshot

Here are a few examples of Jupyter notebooks you’ll find on O’Reilly.

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Whirlwind Tour of Python

A Whirlwind Tour of Python is a fast-paced introduction to essential components of the Python language…

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Machine Learning with Scikit-Learn

This notebook will cover the basics of scikit-learn, a popular package containing a collection of tools for machine learning written in Python.

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Trees and Forests

This notebook gives a conceptual introduction to decision trees and random forests, along with some examples of their usage in scikit-learn.

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Feature Engineering Basics

In this scenario, you’ll learn the basics of feature engineering, including how to use regular expressions, how to encode categorical variables, and how to work with datetimes.