Learn Ionic, 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

Theming Ionic applications is a topic that is often left undiscussed. Ionic has a robust API to theme your application so you don't have to settle with the default styles.

This course will show you how Ionic’s theming system works so that your application can have its unique look. It will explore the built-in utilities, which you can leverage directly.

You'll be introduced to the built-in CSS utilities that Ionic provides, Ionics grid system, and Ionic configuration settings to create a custom look and feel to your App.

Ionic is the beautiful, free and open source mobile SDK for developing native and progressive web apps with ease.

ionic

Style and Theme Ionic v3 Applications

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

Building native mobile applications has traditionally been a completely foreign work flow than web development. Recently frameworks, such as Ionic and React Native, have been come out to create a similar web development workflow in a native environment. 

Ionic 2 builds on top of Angular to offer a complete framework for building mobile apps using HTML, CSS, and Javascript that target native mobile and the browser. We’ll work with the RandomUser API to create an employee directory that will let us view user details, update that information, and pass the data back and forth with Ionic 2 routing.

Angular is a development platform for creating applications using modern web standards. Angular includes a wealth of essential features such as mobile gestures, animations, filtering, routing, data binding, security, internationalization, and beautiful UI components. It's extremely modular, lightweight, and easy to learn.

angular

Building apps with Ionic 2

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

Ionic allows you to build cross platform native applications using Angular. In this course we will convert the Eggly AngularJS application into an Ionic mobile application.

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Building an Angular 1.x Ionic Application

Welcome to the *Ionic Quickstart* series where you will learn how to get started with Ionic from the ground up. We will begin with how to install the required dependencies such as Node.js, Android SDKs and even GenyMotion to installing and running Ionic. By the time you finish, you will know how to create, edit and run an Ionic application. If you are on Windows or Mac and not sure where to start with hybrid mobile development, this is a great place to start!

Ionic Quickstart for Windows and Mac

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

Learn how to install the Ionic 4 CLI and validate its installation.

Install the Ionic 4 CLI

Learn how to create and use the Ionic Floating Action Button (FAB) in your Ionic 4 application.

Position an Ionic 4 Floating Action Button (FAB) Component in an App

Learn how to create and use the Ionic List component in your Ionic 4 application.

Display Data with Ionic 4 List Component

Learn how to create and use the Ionic Badge component in your Ionic 4 application.

Compose Inline Elements with Ionic 4 Badge Component

Learn how to use the Ionic 4 Actionsheet component to allow for more options than the standard Alert component.

Using the Ionic 4 Actionsheet Component

Creating cross-platform web-based applications with Ionic 4 and React is easy thanks to the Stencil compiler and `create-react-app`.

In this video, we look at how to use create-react-app and @ionic/react to make an Ionic 4 + React application

Create an Ionic 4 application with React

Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. Unlike other monolithic frameworks, Vue is designed from the ground up to be incrementally adoptable. The core library is focused on the view layer only, and is very easy to pick up and integrate with other libraries or existing projects. On the other hand, Vue is also perfectly capable of powering sophisticated Single-Page Applications when used in combination with modern tooling and supporting libraries.

Creating cross-platform web-based applications with Ionic 4 and Vue is easy thanks to the Stencil compiler and the Vue.js CLI.

We look at how to combine these together to use Ionic 4 components within our Vue.js applications.

Create an Ionic 4 application with Vue.js

Learn the changes to the Ionic button component that were introduced in version 4. Understand how to change the style and size of the button and how to quickly include an icon.

Using the Ionic Button Component

Understand the basics of the Ionic Card component. See how images are handled within the card, and how to use its sub-components like title, subtitle, and content.

Using the Ionic Card component

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.

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