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.

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

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

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

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

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.

Exploring Sequences and Range() in Immutable.js

Immutable.js iterables offer the reduce() method, a powerful and often misunderstood functional operator on which map(), filter(), groupBy(), etc. are built. The concept is simple: reduce transforms your iterable into something else, that's all. The name is misleading as you may or may not actually "reduce" anything. Let's replicate the groupBy() and filter() methods with reduce to illustrate how it works.

Transforming Immutable Data with Reduce

While Immutable.js offers .is() to confirm value equality between iterables it comes at the cost of referencing each key and value in both objects. For lightning fast equality checks, Immutable.js can produce a hash code based on an iterable's content. If two iterables have the same content, their hash codes will be the same. It's worth noting that this technique is unsuitable for mission critical application development since there is a chance, however slight, that checksums like these might collide. This is outlined here: https://en.wikipedia.org/wiki/Collision_(computer_science)

Lightning Fast Immutable.js Equality Checks with Hash Codes

The Immutable.js Record() allows you to model your immutable data much like you would model data with native Javascript classes or objects. It differs from native classes because it cannot be mutated after it's creation and it *always* has a default value. It's an excellent construct in which to piece together your stores, be them Flux or some other storage implementation. Let's quickly create an Immutable Record().

Immutable.Record() as data models

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. 

Topics

Instructors

Content Type

Free or Pro