Learn Cloudflare, Scikit-learn and Webstorm

Cloudflare Workers is a serverless application platform that enables you to create and deploy functions to Cloudflare's Edge Network.

Unlike other serverless providers that only have regional data centers, Cloudflare's Edge Network consists of hundreds of servers all over the world.

Cloudflare Workers is a powerful platform, and this course serves as a great introduction for what it can do.

Follow along with Kristian Freeman as you build a localization engine that renders data based on the Edge location nearest to the application's user.

After this course, you'll be ready to start experimenting with your own Cloudflare Workers projects. *Need an idea?* *[Check out the suggested Portfolio Project for this course.](https://egghead.io/projects/introduction-to-cloudflare-workers)*

Cloudflare secures and ensures the reliability of your external-facing resources such as websites, APIs, and applications. It protects your internal resources such as behind-the-firewall applications, teams, and devices. And it is your platform for developing globally-scalable applications.

cloudflare

Introduction to Cloudflare Workers

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

Using NPM, you can install Cloudflare Workers' CLI (command-line interface), Wrangler, by typing `npm install -g @cloudflare/wrangler` into your command line. Wrangler allows you to generate and publish Cloudflare Workers projects. We'll also use the `wrangler login` command which will give you a link to authorize Wrangler. This will set up an API token specifically for your Wrangler installation that will allow you to publish and read all the Workers and Workers KV information that you need.

Install and Configure the Cloudflare Workers CLI Wrangler

To deploy a Cloudflare Workers serverless function, you'll need to sign up for a free Cloudflare Workers account at [workers.cloudflare.com](http://workers.cloudflare.com/), and select your unique workers.dev subdomain to deploy your functions to.

Create a Cloudflare Workers Account

Wranglers generate command, `wrangler generate`, allows you to create new Cloudflare Workers projects. Using the URL parameter, you can also generate new projects based on Cloudflare's starter templates, or open-source projects created by other developers, but for now, you will just use the default. To do this you'll only need to give it a name, for example, `wrangler generate my-worker`,  this will create a new project called my-worker. 

Generate New Cloudflare Workers Projects with Wrangler's generate Command

We'll take a look at the default code in `index.js` in a Workers project. We'll take a second to understand how it works before building the rest of our project. 

First will see what our Worker looks like in an actual browser by running `wrangler preview`. In a terminal, and we'll check out how Serverless functions in Cloudflare Workers listen for HTTPS requests via the "fetch" event and respond to them by creating and returning a new Response.

Write Your First Cloudflare Workers Serverless Function

Using `wrangler whoami` in a terminal we'll grab our account id so we can paste our id into the double quotes of `account_id = ""`  inside of `wrangler.toml` file. Using the `wrangler dev` command we can look at our Workers serverless function and test it before deploying it. Once we're ready to deploy, we can use the `wrangler publish` command to deploy it to our unique workers.dev subdomain.

Preview and Publish Your Cloudflare Workers Project

By updating the response body and setting a corresponding Content-Type header, we can render HTML pages from directly inside of our Cloudflare Workers function.

We will update our Cloudflare Worker to return a call to a template function that we define. That template function returns a string of HTML that we will render in the browser.

Render HTML Pages with Cloudflare Workers

Up until now, we've been rendering plain responses, not based on any information coming in with a request. The request.cf object sent with every incoming HTTP request to a Workers function has a ton of interesting information, including the regionality of the request, and which Cloudflare data center is responding to the request. Using this information, we'll render a dynamic HTML page based on where the user visiting our site is in the world.

Render Cloudflare Region Data for a Request Using request.cf

Deploying to your unique workers.dev subdomain is a great way to test your serverless functions before they're ready to go to production. Once you are ready to deploy to production, you can use Wrangler's environment feature to deploy to your zone, or Cloudflare-managed domain, by providing the "-e" (environment) flag when publishing.

You can check this worker out [here](https://hello-world-worker.signalnerve.com/).

Deploy to a Custom Domain with Cloudflare Wrangler Environments

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

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.

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