Learn Puppeteer, Scikit-learn and Webstorm

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

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

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

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

When exploring a new framework or technology, it is nice to have a seed project or sandbox so you can just get in and start playing. Let's build our React playground using npm, browserify, 6to5, and more within WebStorm!

Setting up a React Playground Dev Environment

Lightweight yet powerful IDE, perfectly equipped for complex client-side development and server-side development with Node.js.

webstorm

John Lindquist is a WebStorm wizard. In this lesson, he'll show you some of the tips & tricks he uses to squeeze maximum productivity out of his tool of choice. 

Tips & Tricks

You can easily share your code from WebStorm with others using the "Create Gist" feature. This lesson walks you through how to create a gist from a single or multiples files and also how to use gist.run to share a running version of your code

WebStorm - Sharing a Gist

WebStorm can save you tons of time by managing your imports for you. John walks you through a few key WebStorm importing tips to help speed up your importing workflow.

WebStorm - Managing Imports

A quick tutorial covering installing Karma and running tests from the command line and Webstorm.

Introduction to Karma

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

In this lesson John shows some tricks for speeding up your test suite. He digs into some handy features of WebStorm, including the *ddescriber* plugin, as well as some alternative methods for Jasmine for selectively running your tests.

Speed Testing with WebStorm and Jasmine

In this lesson, we'll walk through installing a custom color scheme into our [WebStorm](https://www.jetbrains.com/webstorm/) IDE. Dayle Rees's [daylerees/colour-schemes](https://github.com/daylerees/colour-schemes/blob/master/jetbrains/peacocks-in-space.icls) has a great collection of themes ready to install in different formats for various editors.

For this example, we will install the [Peacocks In Space](https://github.com/daylerees/colour-schemes/blob/master/jetbrains/peacocks-in-space.icls) theme into an OS X [WebStorm 11 EAP](https://confluence.jetbrains.com/display/WI/WebStorm+EAP) installation. _Note: This process will be very similar (or identical) for previous versions of WebStorm and other editors in the [JetBrain's family of editors](https://www.jetbrains.com/)._

WebStorm - Installing Custom Color Schemes (dayleress/colour-schemes)

WebStorm can easily help you set up a new Angular 2 project with never even touching a terminal. This lesson shows you all the neat GUI tricks you need to know to get up and running.

WebStorm - Setting Up Angular 2

grunt

In this lesson you will install GruntJS and get up and running with your first task, initial configuration, and how to get Grunt methods auto-completing in WebStorm.

Introduction to Grunt

Linear regression is a linear model that is used for regression problems, or problems where the goal is to predict a value on a continuous spectrum (as opposed to a discrete category).

We’ll use linear regression to estimate continuous values. In this case, we’ll predict house prices in Boston. We'll also look at how to visualize our results with matplotlib, and how to evaluate our models with different metrics for regression problems.

Use Linear Regression To Estimate Continuous Values with Python and Scikit-learn

Despite its often confusing name, logistic regression is a **linear** model that is used for **classification**, or estimating discrete values.

We'll use an inbuilt scikit-learn dataset of iris data to classify irises into three categories. We'll also look at metrics and tools to evaluate our classification models, including the accuracy score, classification report, and confusion matrix.

Predict Categories in Python using Scikit-learn's Logistic Regression module

We’ll continue with the iris dataset to implement support vector machines, which can be used to find more complex boundaries for classification or regression problems. 

More about Support Vector Machines can be found at [scikit-learn](http://scikit-learn.org/stable/modules/svm.html).

There are several types of kernel function that can be used with SVMs. Scikit-learn supports these kernels:

-linear

-polynomial ('poly')

-rbf (radial basis function) 

-sigmoid 

Custom kernels are also supported. `Rbf` is the default kernel type.

A good overview of kernels can be found [here](https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html), or at the [scikit-learn page](http://scikit-learn.org/stable/modules/svm.html#svm-kernels).

Use Support Vector Machines To Find Complex Decision Boundaries with Scikit-learn

We’ll continue with the iris dataset to implement k-nearest neighbors (`KNN`), which makes predictions about data based on similarity to other data instances. We'll visualize how the `KNN` algorithm works by making its predictions based on its neighbors' labels. We'll also examine the confusion matrix a bit further.

`KNN` can be used for both classification and regression problems. 

`KNN` is good for low dimensional data (data without too many input variables). It is not good for unbalanced data sets, and it can be computationally expensive.

Use K-nearest Neighbors To Find Similar Datapoints with Python and Scikit-learn

We’ll learn about decision trees, also known as CART (classification and regression trees), and use them to explore a dataset of breast cancer tumors. 

We'll also see how to visualize a decision tree using `graphviz`.

Implement Decision Trees in Python with Scikit-learn

We’ll return to the iris dataset to see how to use k-means clustering, an unsupervised learning algorithm, to create  categories for data that doesn't have labels. We'll also visualize these clusters using matplotlib.

More on K-means can be found at [Scikit-learn](http://scikit-learn.org/stable/modules/clustering.html#k-means).

Find Clusters of Data with K-means Clustering in Python and Scikit-learn

In this lesson we are going to set up our basic app, set our environment variables, define our targeted viewport, and install Jest, Puppeteer, and Faker. We'll use these features to write our end to end tests. 

Setup and Install Dependencies for Testing with Google's Puppeteer

Now that we have our setup and environment ready for using Puppeteer to test our create-react-app, we can write our first tests. We will test our h1 and navigation, to make sure that they load correctly. In this lesson, we will be able to watch puppeteer open up a browser and evaluate our tests in real time. 

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