Learn Docker, Netlify and Python

While "It works on my computer" might give you an excuse to close a bug ticket in the backlog, it doesn't really work when you're working on a team.

You need to ensure that your application can run regardless of what hardware your teammates are running or libraries they have installed.

Docker solves this problem by collecting everything your application needs to run into a container.

Containers aren't just for source code. They can hold configuration, scripts, and even their own filesystem. It's similar to a Virtual Machine, without having to bring a full operating system along for the ride.

Because containers run in isolation, there is some configuration involved to allow for files to be saved and containers to communicate with one another.

One of the most useful ways to get your head around Docker is to see it in action.

Follow along with Joel Lord as he works through the process of preparing an application to run in separate containers for the frontend and backend of an application that searches for gifs and re-encodes them with a caption supplied by the user.

You'll see how to configure the separate containers to communicate with one another, work with environment variables, and persist data to your local machine. Along the way, you'll pick up some tips on useful commands and bash scripting. Finally, you'll learn how to use Docker Compose to make it easy to run multiple containers simultaneously.


Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

Containerize Full-Stack JavaScript Applications with Docker

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

How does e-commerce work on the Jamstack? 

Jason Lengstorf and [Thor 雷神](https://twitter.com/thorwebdev) teamed up on _Learn With Jason_ to [build a serverless Stripe Checkout implementation](https://www.learnwithjason.dev/sell-products-on-the-jamstack). This collection is a cleaned up, egghead-style tutorial for selling products on a Jamstack site using Netlify Functions and Stripe Checkout.

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

Sell Products Using Stripe Checkout and Netlify Functions

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

A short list of screencasts to cover essentials of [Gatsby](https://www.gatsbyjs.org/) to get started and have enough knowledge to become dangerous. 

The content comes from multiple instructors that have each covered different essentials, this cherry picks some of the most important concepts.

Videos in this collection cover these topics:
- installing plugins
- creating pages
- navigating between pages
- adding optimized images
- theming your styles
- deploying your site

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

Up and Running with Gatsby

This application serves as an introduction to building products with a JAMStack and Serverless approach. We cover everything from the beginning:

* starting with the Gatsby client
* handling authentication with Netlify Identity
* Shipping a GraphQL server on Netlify Functions
* Building in access control with a serverless capable database

It uses tools that remove as many of the barriers as possible while also allowing upgrading in the future. Netlify Functions can grow into AWS Lambda, Netlify Identity can grow into Auth0 or Cognito, and FaunaDB can grow into DynamoDB.

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

This collection is a sequel to the [Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB](https://egghead.io/playlists/building-a-serverless-jamstack-todo-app-with-netlify-gatsby-graphql-and-faunadb-53bb) collection. In this collection we take the TODO application we built and convert it to run using Netlify Identity, AWS Lambda (using the serverless framework) and DynamoDB. We cover

* Fauna vs DynamoDB and when to use each
* Setting up AWS accounts
* Creating DynamoDB tables and data modeling differences between Fauna and Dynamo
* Converting our Netlify Functions deployment to a Serverless Framework deployment
* Implementing Custom authorizer functions on AWS

It uses tools that remove as many of the barriers as possible. Netlify Functions grows into Serverless Framework adn AWS Lambda, Netlify Identity is kept around, and FaunaDB can grows into DynamoDB.



FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

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

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

In this course we will take a simple Node.js app that connects to a MongoDB database and uses an Express web server, and learn how to setup a full software development deployment process as well as how to properly “Dockerize” the app. 

We’ll step through setting up proper version control techniques, managing configuring within the environment, setting up proper build/deploy/run processes, and all with abiding by Docker best practices. We’ll also make sure our deployment is setup for scale, and have consistent development practices in place for reliable app deployments.

Build a Twelve-Factor Node.js App with Docker

Zeit’s mission statement is to ‘Make Cloud computing as easy and accessible as Mobile computing.’ To that end, they’ve released some pretty amazing and powerful tools. In this course we’re going to take a look at `now`, their deployment tool, to see why it’s hands-down the simplest way to launch node applications, docker containers and even static files to the cloud.


We’ll also dig into some of now’s more advanced capabilities, including host aliasing, environment variables and integration with hosted databases.

Deploy Web Apps with Zeit Now

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.

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

Next.js is a React framework that provides a solutions to problems such as:

- 🌟 bundling & transpiling your React code
- 🌟 code splitting
- 🌟 being able to switch between client side rendering & server side rendering depending on the page (e.g. landing page vs. settings page)
- 🌟 client-side routing

In this collection we're going to learn how to harness the power of Next.js + TypeScript and how to work with Netlify in order to deploy our site for others to enjoy!

Learn how to:
- 🔥 Create a new Next.js project
- 🔥 Add TypeScript support
- 🔥 Create styled components with Emotion
- 🔥 Use both static and dynamic routes
- 🔥 Generate fully typed static pages
- 🔥 Set up Netlify deployments
- 🔥 Add a Netlify Form to our site
- 🔥 and more!

Next.js is a minimalistic framework for server-rendered React applications.

Build a site from scratch with Next.js, TypeScript, Emotion and Netlify

In this lesson, you will learn how to push your website code to Github and Build and deploy it using Netlify

Deploy a Gatsby website on Netlify

Netlify will automatically build your Gatsby website whenever you push new code to your repository but what about content changes in Contentful. In this lesson, you will learn how to use Contentful webhook feature to trigger a Netlify build whenever you publish or delete new content on Contentful

Trigger Netlify Builds when content changes in Contentful

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

Kubernetes is an API to a computer. Multiple computers actually. Here we'll talk about what K8s is, how you can choose between the plethora of K8s related hosting options, and what the process looks like for getting some containers running.

WTF is Kubernetes (K8s)

We set up the build command, deployment directory, and other necessities to get our Gatsby site auto-deploying on commit to Netlify.

Setting up continuous deployment for a Gatsby site in yarn workspaces using Netlify

Netlify Identity allows you to manage and authenticate users on your site or app, without requiring them to be users of Netlify or any other service. You can use this for gated content, site administration, and more.

In this lesson, we integrate Netlify identity with Gatsby using React hooks.

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