Learn Github, Protractor and Python

SSH provides a secure way for your to [connect to and authenticate with GitHub's servers](https://help.github.com/en/github/authenticating-to-github/about-ssh). While HTTPS connections use usernames and passwords as credentials, SSH instead uses public and private key pairs. These can be generated on your system using a CLI like [`ssh-keygen`](https://help.github.com/en/github/authenticating-to-github/generating-a-new-ssh-key-and-adding-it-to-the-ssh-agent) and then associated with your GitHub account using the public key.

In some cases, you may need to connect to more than one GitHub account from the same system. For example, you may have [separate accounts for personal and professional projects](https://ryanharris.dev/managing-ssh-keys-for-github/), which can be difficult to manage manually. Using `ssh-agent` and a SSH configuration file, you can alleviate this pain point by automatically determining which key to use based on your GitHub repository's `git` URL.

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.

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Secure GitHub connections with SSH

Github Actions are an awesome tool from Github that allows us to automate tasks using code-based workflow configuration files. There's also a large marketplace that comes stocked with many useful tools that are ready out of the box.

But sometimes the options available don't completely solve our needs or maybe we're working with a new technology that's simply not available as an Action yet. Luckily, Github allows anyone to create their own Actions using common technologies like Javascript via Node.

We'll learn how to create a new Javascript-based Github Action running on Node. We'll first walk through a simple Hello World example, learn how to test it locally, version control it, use it in the wild, and move on to more advanced solutions that include using packages from npm!

GitHub fosters a fast, flexible, and collaborative development process that lets you work on your own or with others.

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Create a New Github Action to Automate Code Tasks with Javascript

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

Handy tips for boosting your productivity in GitHub. These are bite-sized things you can take advantage of today to boost your efficiency by 1% or more. What are you waiting for? Check 'em out! 

GitHub Tips & Tricks

GitHub Actions are a powerful workflow tool that allows you to write and use custom code to execute workflows. It's like a serverless CI/CD platform built directly into GitHub. We have access to GitHub's registries, our own repos, multiple platforms (Mac, Windows, Linux), runtimes (VMs and Containers), and languages (JS, Rust, Go, etc) to perform any action we want. We can trigger workflows using GitHub's own events, our custom events, or even scheduled events like a cronjob.

GitHub Actions at Work

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

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

Publishing a JavaScript library for public use requires some extra steps. You need to think about how people will use the library. From end users, to contributors your library now has a variety of people outside of yourself potentially making use of the code that you've released into the wild.

From Github and npm, to releasing beta versions, semantic versioning, code coverage, continuous integration, and providing your library with a solid set of unit tests, there are a ton of things to learn.

This series will guide you through a set of steps to publish a JavaScript open source library.

You might also enjoy [this article about contributing to open source](https://egghead.io/articles/get-started-contributing-to-javascript-open-source).

How to Write an Open Source JavaScript Library

“Feel free to submit a PR!” - words often found in GitHub issues, but met with confusion and fear by many. Getting started with contributing open source is not always straightforward and can be tricky. With this series, you’ll be equipped with the the tools, knowledge, and understanding you need to be productive and contribute to the wonderful world of open source projects. Much of this series speaks about GitHub, but most of the concepts are generally applicable to contributing to any open source project, regardless of where it’s hosted.

So enjoy the course and start contributing to the projects you use and love today!

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

How to Contribute to an Open Source Project on GitHub

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

In this lesson, we’ll apply our knowledge of `getopts` and create a script that opens a PR on Github, all without leaving your terminal. We’ll use `getopts` to take 3 options: username, password or token, and PR title. We’ll use any input after that as the body of the PR. We’ll validate the input and then use `curl` to POST it to Github. 

Create a Bash Script to Open a Pull Request on Github using getopts

In this video you'll learn Git-based deployment for web applications for both front-end and back-end resources using AWS Amplify and the Amplify Console.

Hosting & Continuous Deployment for Front End and Serverless Back Ends using AWS Amplify

GitHub Actions can be triggered to run on a schedule using cron syntax. In this video we go over what that means and how to write cron syntax. We also show a tool and a series of examples to aid in writing and debugging cron.

* https://crontab.guru/

Scheduling GitHub Actions with cron

GitHub Actions can feel daunting. Here's a walkthrough setting up your first action that runs some command line scripts on every push to master.

Using your first GitHub Action

GitHub Actions are a powerfully flexible workflow automation tool that lets you respond to GitHub events, your own arbitrary events (like webhooks), or scheduled events with workflows that run on Windows, Mac, or Linux in VMs and Containers. It's like a serverless CI/CD platform built into GitHub.

Introduction to GitHub Actions

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

If you make a mistake in a commit message but HAVEN'T pushed it yet, you can change that commit message with --amend:

`git commit --amend -m "New message"`

This won't change any of the files in your commit - but will rewrite the commit with the new message.

Change a Commit Message that Hasn't Been Pushed Yet

If we make a change to the same function both locally and remotely, then when we try to pull down the remote changes, we'll run into a conflict - and git won't pull the remote changes down.

So first, we'll use `git stash` to stash the local changes, and then `git pull` to get the remote changes.  To apply the stashed changed, we'll then use `git stash pop`

Since we changed the same function however, we'll get a merge conflict!  So we'll fix the merge conflict, and then add and commit that change.

Finally, we'll drop the change from the stash with: `git stash drop stash@{0}`

Use `git stash` to Save Local Changes While Pulling

If we try to make a pull request on github that has a conflict, there are a few ways to fix it.

We're going to first back out of the pull request, and then merge `master` into our feature branch.

We'll manage the merge conflict locally, and then do a new pull request (which will not have the conflict)

Fix a Pull Request that has a Merge Conflict

We've made a pull request and now we can clean up the branches by deleting the feature branch.

Branches are just pointers to commits - so we can safely delete branches without losing the underlying commits (once the commits are merged back into master).

So we'll use the github interface to delete the branch remotely, and to delete it locally we'll use `git remote prune origin --dry-run` and then `git remote prune origin`

That will tell us that the remote is gone, and we can finally clean up the feature branch with: `git branch -d feature-branch`

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