Learn Immutable, 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.

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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.

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Fully Connected Neural Networks with Keras

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.

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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

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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

im·mu·ta·ble i(m)ˈmyo͞odəb(ə)l/ adjective - **unchanging over time or unable to be changed**.

Mutating data is the source of never ending headaches in software systems, and JavaScript code is no exception. Immutable data structures, pure functions, uni-directional data flow... you've probably been hearing these terms.

Immutable.js provides us the data structures and utilities that we need to start introducing this powerful concept of immutability into our applications.

Immutable.js provides many Persistent Immutable data structures including: List, Stack, Map, OrderedMap, Set, OrderedSet and Record.

immutable

Learn how to use Immutable.js

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

Sometimes our update logic is asynchronous. In this lesson we will investigate two ways in which we can write asynchronous update logic. 

…But, we will also discuss why asynchronous producers should be avoided, and how that can be done by better separating our logic. 

This lesson also demonstrates the “createDraft” and “finishDraft” APIs

Write Asynchronous Producers in Immer and Why You Shouldn’t

In this lesson we will send patches over a WebSocket to our server, and distribute them again from there.

Create a Simple WebSocket Server to Distribute Patches from Immer

Sometimes you get strings instead of objects and you have to parse them out. Here's an example where the raw data I got from a Twitch chatbot message wasn't useful to me until I parsed it into a dictionary.

Turning a key/value string into a dictionary in Python

Puppeteer is a Node library which provides a high-level API to control headless Chrome or Chromium over the DevTools Protocol. It can also be configured to use full (non-headless) Chrome or Chromium.

puppeteer

When taking screenshots, it's important that the browser is big enough to show all of the content in the DOM that we want to screenshot. We can do this by setting the viewport.

Setting the viewport on a Playwright headless browser

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

Read from a File in Python

Learn how to create a tuple in Python. A tuple is an immutable data type that contains several items similar to a list. Items in a tuple can be of any data type.

Create a Tuple in Python

Learn to create a one item tuple. When creating a one item tuple, a comma has to be included after the item. Otherwise, your tuple will not return tuple as its data type. 

Create a One Item Tuple in Python

Tuples are immutable so there is no way to add or replace items in tuples. There is a way to get around this by adding two tuples into one. 

Understand Immutability with Python Tuples

Immutable.js offers methods to break  immutable structures into subsets much like Array--for instance it has the all powerful slice()--and unlike Array it offers functional methods like take() and skip(). You get the best of both the imperative and functional worlds.

Working with Subsets of an Immutable.js Map()

Equality checking deeply nested objects is painful. Fortunately Immutable.js makes this task easy with its .is() and .isSubset() methods. Let's see how we can take two different Immutable Maps() and check for equality.

Equality Checking with .is() and More

Immutable.js offers the fromJS() method to build immutable structures from objects and array. Objects are converted into maps. Arrays are converted into lists. The fromJS() method can also take a reviver function for custom conversions.

Using fromJS() to Convert Plain JavaScript Objects into Immutable Data

Understanding Immutable.js's Map() and List() structures will likely take you as far as you want to go with immutable programming. They have only small semantic differences between each other and the remaining structures in the Immutable.js family. Sequence, however, has one major difference: it's lazy--which opens a new realm of functional possibilities. Let's write a simple sequence to start.

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