Learn Android, 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

A lot of the time, web apps are built primarily with the desktop layout in consideration, and the mobile layout suffers as a result. In this course we are going to discuss using features of user interface which are optimised for mobile devices.

We are going to discuss CSS features that make a big difference and build entire components that work great on mobile devices and browsers. We are going to take into account browser support to ensure our users are getting the experience we want them to.

More and more of our users depend on their mobile device to use our web apps. With this in mind, we are going to look at how to optimise the components we build for smaller viewports, and how to ensure our customer gets an optimised version of our web app for their device.

We are going to discuss different ways we can view, test, and debug our web app in a mobile setting so that we know what we are seeing is what our user is seeing, because sometimes a desktop browser emulating a mobile device just isn’t enough.


HTML 5 is the structure of our web pages. It is the markup that represents the DOM (document object model).

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Optimize User Experience for Mobile Devices and Browsers

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.

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Up and Running with AWS Amplify Functions

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

Open source framework for building truly native mobile apps with Angular, TypeScript or JavaScript.

nativescript

We will learn how to add an Android library to our project and translate it’s Java api to NativeScript. For this lesson, we will look at [Filestack’s Android SDK](https://github.com/filestack/filepicker-android) built with Gradle. Specifically, we will cover how to immediately start working with the library even when a [NativeScript](/q/nativescript) plugin does not exist for it.

Use an Android library in NativeScript for Angular using Java apis

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

Java has been around a long time and when it comes to Android, you will encounter some structures that may throw you for a loop on how to code against with NativeScript. Let’s examine a few common Java/Android api constructs you may encounter and how to translate them to NativeScript. Learn how to get references to local files in your app using the ‘file-system’ module as well as how to take advantage of NativeScript’s Observable class to send notification events from one class to another. Lastly, look at considerations with Angular’s NgZone when handling your native libraries data events to update component view bindings.

Translate common Java/Android APIs into NativeScript for Angular Android apps

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

One of the exciting benefits you have with NativeScript is the ability to write native code (Java and Objective C) directly in JavaScript/TypeScript allowing you to create a consistent high level api over top of both underlying iOS Objective C/Swift and Android Java apis. In this lesson, we will look at how to design a consistent api that taps into the appropriate native api’s on the target platforms using underlying native libraries.

Develop a consistent plugin api across your Android and iOS NativeScript app

To prepare your development environment to code against native apis such as Objective C, Swift and Java, let's learn how to [install tns-platform-declarations](https://www.npmjs.com/package/tns-platform-declarations) to provide rich intellisense support for all the native iOS and Android api’s. This lesson will cover how to modify your tsconfig.json and references.d.ts properly to ensure working with native api’s is smooth and as helpful as possible.

Prepare for native iOS and Android api intellisense with NativeScript

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

Building a custom NativeScript XML view component for Android is uniquely different than it’s iOS counterpart because of the Android Context which must be considered. Let’s look at how to properly build a custom view component based on an underlying native view widget. We will also look at what happens when our include.gradle file is not setup properly including handling problems with minimum sdk targets with plugins. Specifically, we will implement this Android widget representing the [Google I/O 2016 clock](https://github.com/lypeer/GoogleClock). Learn how to create a custom AndroidManifest.xml inside your plugin to merge with your app's manifest to override certain characteristics. Read detailed documentation about the [Android app manifest file](https://developer.android.com/guide/topics/manifest/manifest-intro.html).

Build a custom NativeScript view component for Android

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.

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