Learn Docker, Mac 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

Bash makes automation easy. Just a few lines of code can take care of repetitive tasks for you and your teammates. 

In this course, we’ll dive deep into the automation potential of bash. You’ll learn to leverage common hooks that open up interesting automation possibilities, like git hooks, adding functions and aliases in your bash_profile, and running scheduled jobs in launchd.

For example, we’ll write a git hook that checks package.json for changes, and if there are changes, the hook runs npm install for you automatically. 

Along the way, you’ll also learn:

- more advanced bash commands like exec
- syntax such as redirecting stdout and stderr to the same destination
- timesaving tricks like brace expansions

Automation is all about saving you time — and at only 52 minutes, this course is well worth yours. Enjoy!

Bash is a Unix shell and command language written by Brian Fox for the GNU Project as a free software replacement for the Bourne shell. Bash is a command processor that typically runs in a text window, where the user types commands that cause actions. Bash can also read and execute commands from a file, called a shell script.

bash

Advanced Bash Automation for Web Developers

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.

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

Up and Running with AWS Amplify Functions

Joe Previte is an active egghead instructor and blogger. This is a collection of screencast versions of selected blog posts. You can find his blog here: [joeprevite.com](https://joeprevite.com/)

Joe's Blog Posts as Screencasts

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

Learn VSCode with Ahmad Awais (Open Source JavaScript DevRel ❯ Google Dev Expert ❯ Author of 3rd most popular VSCode theme called 🦄 Shades of Purple).

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

VSCode

`launchd` is a robust scheduled job automation tool on macOS that allows you to schedule a task to be run at recurring times. It's macOS's take on Linux's `cron`. In this lesson, we’ll schedule a simple bash script that empties the Trash to run every Monday morning at 10am. 

First, we’ll create a plist file (a plist file is just XML) with our job configuration. In here, we’ll specify our bash script as the program to run. Then we move plist file to the `~/Library/LaunchAgents` folder. Then we’ll use launchctl to load our configuration file, which will make the job active. We'll manually activate our job to ensure it's working with `launchctl kickstart`. 

Schedule Timed Jobs on macOS with `launchd`

AppleScript is a peculiar language with a COBOL-like desire to read like English, but it's the key to automating UIs on the Mac. We'll use it together with bash to make Terminal open a new window to run any shell command.

Run a Command-line App in a New Terminal Window using Bash and AppleScript

In [the previous lesson](https://egghead.io/lessons/bash-run-a-command-line-app-in-a-new-terminal-window-using-bash-and-applescript "Run a command-line app in a new terminal window") we created a bash script that runs a given command line in a new Terminal window. Now we'll see how to use Automator to make an instance of that available as a menu item in Finder.

Turn a command-line app into a menu item in Finder

In [the previous lesson](https://egghead.io/lessons/mac-turn-a-command-line-app-into-a-menu-item-in-finder) we saved a shell script as a service for Finder. This service takes in files as arguments and opens them in Vi. Now we'll save it as a real Mac App.

Turn a command-line app into a Mac App

Smooth your workflow by opening Finder from Terminal and vice versa at a specific folder.

Jump from Terminal to Finder and back again

I can make a subfolder of the folder that is open in Finder,
but there's no convenient way to make a subfolder of amy other folder that
is showing. Let's add a Finder service to take care of that.

Create a new subfolder of any selected folder in Finder with AppleScript

Create a Finder service that takes whatever you've selected, lets you navigate to a destination folder, and moves them there.

Create a service to move items in Finder to a user-selected folder with AppleScript

Visual Studio Code is an integrated development environment (IDE) built and maintained by Microsoft. It is offered free of charge.

vscode

Developers spend 80% of their day looking at the code. It's only fair if that code looks good with a custom programming font. In this lesson, learn what options you have for both free and premium custom code fonts.

Custom Programming Fonts with VSCode

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

screen-reader

Learn how to access the VoiceOver training and utility preferences on MacOs and how to make customizations to the screen reader.

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