Learn 11ty, Puppeteer and Python

OpenGraph images are the images you see when you paste a link into a social platform and it "unfurls" into an image, title, and description. This happens on Twitter, Discord, Slack, and many other platforms. This collection goes over everything from designing OpenGraph images in Figma, to implementing them in CodeSandbox, to returning headless browser screenshots from Netlify Functions, and finally using Cloudinary as a write-through cache. 

You will come away from this collection with the ability to ship an API that you can use on any of your sites, and also on-demand, that can generate images for not only OpenGraph unfurls, but also for Instagram, GitHub, and more. Using headless browsers with playwright to generate our images means we get full access to all the responsive power of CSS and all the logical power of JS to handle layout, importing assets like pngs, choosing different fonts, and more.

The image below is generated via the project you'll build and deploy in this course

![An example opengraph image](https://relaxed-payne-d1bfbe.netlify.com/opengraph?title=Building%20an%20OpenGraph%20image%20generation%20API%20with%20Cloudinary%20Netlify%20Functions%20and%20React&tags=figma,netlify%20functions,playwright&author=chrisbiscardi)

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Building an OpenGraph image generation API with Cloudinary, Netlify Functions, and React

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

python

Fully Connected Neural Networks with Keras

Eleventy, or 11ty,  is a simple yet very powerful static site generator. It does not require any config to get started.

This what it will take you to get an 11ty project running.

```
npm install -g @11ty/eleventy
echo '# Page header' > README.md
eleventy
```

After watching this collection you will be able to:

* Bootstrap a simple 11ty website
* Use 11ty layouts
* Chaining multiple 11ty layouts
* Use 11ty Collections
* Create pages from data

...and much more

Eleventy was created to be a JavaScript alternative to Jekyll. It’s zero-config by default but has flexible configuration options. Eleventy works with your project’s existing directory structure.

Eleventy uses independent template engines. We don’t want to hold your content hostage. If you decide to use something else later, having your content decoupled in this way will make migration easier.

11ty

Getting Started with Eleventy

End-to-end testing assures you of a few things: that all the integrated pieces of an application properly function, all those pieces work together as expected, and your tests include and simulate real-user scenarios — something unit and integration tests just don't do.

This course introduces you to two great tools for E2E testing: 

* Puppeteer opens and runs applications in a Chromium browser and performs the actions it's given.
* Jest asserts that the integrated pieces work as expected and that add-on libraries like Faker and pixelmatch take our tests to the next level.

Over 9 lessons, we will create common application features and use Puppeteer and Jest to run different types of tests against them. You'll get practice using these tools to render content, mimic user activity, automate tests, and measure the overall performance of your application.

jest

End to End Testing with Google's Puppeteer and Jest

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

scikit-learn

Introductory Machine Learning Algorithms in Python with scikit-learn

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Up and Running with AWS Amplify Functions

Start from a blank project and build up to an Eleventy site that includes a blog collection and is prepared to source content from a headless CMS. You'll also learn how to add Sass as the styling solution, complete with triggering 11ty to re-compile when the Sass changes.

These lessons will help explain key 11ty features and concepts to help you make the most of this amazing static site generator.

By the last lesson, you'll have learned how to:
- define custom `input` and `output` directories
- create a layout that accesses front matter variables
- watch for Sass changes and include CSS in the build proces
- create an 11ty collection using `tags`
- define front matter for an entire collection
- make and apply a filter to a collection
- generate pages from an external data source, such as a CMS or API

Build An Eleventy (11ty) Site From Scratch

Learn how to make flexible customized Twitch Scenes using the static site generator Eleventy (11ty), CSS, and ComfyJS.

Throughout this course, you'll create scenes for preshow, host, and main desktop display streaming purposes. Using ComfyJS, we'll add embedded chat and be able to create responses to chat commands. Finally, we'll see how to actually setup the scenes in OBS to prepare to use them for your stream.

Bonus: you'll learn a bit more about CSS grid layout, and also the "cicada principle" to be able to show the commands randomly across the scene!

Develop Custom Twitch Scenes and Setup in OBS

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Misunderstanding scope can cause problems in your application. Watch this lesson to learn how Python scope works and the hidden implications it presents. Local scope, nonlocal scope, and global scope variables are demonstrated and explained.

Understand Scope in Python

A module is a function extracted to a file. This allows you to import the function and use it in any other code you may write. You’ll learn how to create modules, import them, and make them stand-alone as you learn what `if __name__ == “__main__”` means in Python.

Reuse Code in Multiple Projects with Python Modules

Why should you have to write all the code? Why can’t you leverage code written by others? I’ve got great news: you can. You’ll learn how to search pip, Python’s package installer, to find and install packages to add new features and functionality to your application.

Manage packages with pip in Python

Object oriented classes work much like classes in other languages. Learn how to create them and use them, learn the difference between class variables and instance variables, creating class methods, and learn how to create classes that inherit from other classes.

Use Python Classes

Getting input from the user via a console command can be done with `input` in Python. Learn how to implement it and validate user input in this lesson.

Read Input in Python from the Console

Exceptions cause your application to crash. Handling them allows you to recover gracefully and keep your application running. Learn how to handle exceptions, find what exceptions you should be handling, and exit gracefully in this lesson. You will also learn how you should *not* handle exceptions and when it is better to let your application crash rather than swallowing an exception. 

Handle Exceptions to prevent crashes in Python

This lesson will introduce arithmetic operations in Python. In addition to basic addition, subtraction, multiplication, and division, you will learn about the differences in division between Python 2 and Python 3.

Perform Math Operations in Python

The tuple is a new data structure to Python, and is an immutable sequence of values separated by commas. Learn how to create them in this lesson.

Create Immutable Values in Python with Tuples

In this lesson you will learn how to install Python. If you use OS X or Linux, you already have Python 2 installed. I will show you how to install Python 3 on OS X, Linux, and Windows.

Install Python

Virtual Environments ensure that dependencies from one Python application don’t overwrite the dependencies of another application. In this lesson, you will learn how to create a virtual environment, switch between virtual environments, and manage dependencies within a virtual environment.

Manage Dependencies with Python Virtual Environments

List comprehensions provide a concise way to create new lists, where each item is the result of an operation applied to each member of an existing list, dictionary or other iterable. Learn how to create your own list comprehensions in this lesson.

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