Learn Cloudflare, Nuxt and Python

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.

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Introduction to Cloudflare Workers

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

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

This course walks you through the steps of creating a News web app using Vue.js and the Nuxt framework. You will learn how to create dynamic pages for each section of your application and load, store, display, filter, and style the data. Then end result will be a News app with multiple category pages, comments for each section, and user pages.

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.

Create a News App with Vue.js and Nuxt

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

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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

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.

Turning a key/value string into a dictionary in Python

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

Read from a File in Python

Learn how to create a tuple in Python. A tuple is an immutable data type that contains several items similar to a list. Items in a tuple can be of any data type.

Create a Tuple in Python

Learn to create a one item tuple. When creating a one item tuple, a comma has to be included after the item. Otherwise, your tuple will not return tuple as its data type. 

Create a One Item Tuple in Python

Tuples are immutable so there is no way to add or replace items in tuples. There is a way to get around this by adding two tuples into one. 

Understand Immutability with Python Tuples

In this video you'll learn how to deploy a static Nuxt site to AWS's global edge CDN in less than two minutes. We'll start from scratch, creating a new Nuxt site using npx and walk through the build configuration to set up the deployment target for Jamstack / static site hosting. From there, we'll initialize a new Amplify project using the Amplify CLI and walk through the settings to configure the proper build command for Amplify hosting. Finally, we'll deploy and view the live site. Once we're finished, we'll delete the project and the hosting service.

Deploying a Nuxt Site to AWS with the Amplify CLI

In this video you'll learn how to use Amplify hosting to deploy a Nuxt site from a Git repository. We'll begin by walking through the steps of creating the deployment and configuring the correct build settings to enable a static build. Once the site is deployed, we'll view the live site hosted on AWS.

Deploying a Nuxt Site to AWS from a Git repository with Amplify hosting

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.

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