Learn Git, Natural and Serverless

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

This workshop will start by breaking down the four states in which a file can exist, and we'll build up our mental model of git from there. Then, we'll look at how to move files between those states in different ways.

Once we've developed a new model for how git operates, we'll be able to purposefully get ourselves into some sticky situations, and then gracefully recover from them.

If you can handle git add/commit/push, but not much more - then this workshop is perfect for you.

You can finally get some proper git training, and will never have to worry about losing data or messing up the repo again!

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.

Fix Common Git Mistakes

Knowing your everyday toolset increases your productivity as a developer. And Git is one of those tools you use on a daily basis. Mastering Git is tough; it has hundreds of different commands, often even inconsistent at how you pass in flags and configuration options. On the other side, there are a lot of good Git courses out there already, explaining all of these commands in detail. 

With this course, I aim at a different goal. You will walk through a series of scenarios which you’ll most commonly encounter in your daily work life as a developer. I’m talking:
 - updating your feature branch with the latest changes from master
 - polishing your git history to make it ready for being peer-reviewed
 - moving a set of commits to another branch
 - undoing accidental commits

The goal is not to cover everything, but those tasks that will greatly improve your daily productivity with Git.


Productive Git for Developers

In this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

As modern web developers, we are likely to use git on a daily basis. It's a wonderful distributed version control system, and with only a few commands in our toolbelt it is quite powerful and convenient.

In this course, you will learn the basics of using git, as well as some more advanced commands that you should find useful!

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/practical-git-for-everyday-professional-use-notes).

Practical Git for Everyday Professional Use

“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!

How to Contribute to an Open Source Project on GitHub

Create a serverless application and deploy it in under thirty minutes!

We'll be using Netlify, Vite, TypeScript, and React in our project. This stack synergizes extremely well for rapidly developing professional-quality applications. 

If you haven't heard of Vite, it's the French word for quick! It's also a build tool that consists of a dev-server and an extremely fast bundler. It makes for a rapid development loop.

As for the rest. Netlify makes hosting fast and easy with serverless functions and continuous deployment. React enables you to quickly create reusable components. And TypeScript helps you write cleaner code that is easier to refactor. 

So get started and learn how to use this stack in your own applications!

Create a TypeScript powered serverless React application

Most projects have automatically generated files or folders from the operating system, applications, package managers etc. Usually, we don't want to include these types of things in our remote repos because they can clutter the git history/storage and are not applicable to everyone that works on the project. In this lesson, we show how to create a `.gitignore` file to ignore files and folders from being tracked by git.

Practical Git: Remove unnecessary git tracking with .gitignore files

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

When working with Git, the most common thing developers do is stage, commit, and push the changes they make to a code base - this "edit -> stage -> commit -> push" cycle is the main workflow when using Git; it lets developers make "snapshots" of changes to their codebase which they can share and revert back to. In this lesson, we make some changes to our codebase and then `git add` (stage), `git commit` (store in history), and `git push` (sync with our remote repository) those changes. We also use `git status` along the way to see an update of where our repository is at.

Practical Git: Capture code history snapshots with git add/commit/push

`git pull` lets us get the latest changes from our project's remote repo (most likely from co-workers or other developers working on our project) and merge (combine) them with our local code. It's kind of like Dropbox for code, but you tell git when you want it to get the latest changes from the remote when it is most convenient for you. In this lesson, we use `git pull` and discuss how it is a shortcut for running `git fetch` and `git merge`.

Practical Git: Sync local and remote repos with git pull

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

Identify the most important words in a document using tf-idf in Natural

When working on a project, it is much easier to work on features and bugs in isolation of the rest of the project. We can do this with git branches; a branch is a copy of the working directory, staging area, and project history; we create a branch, then check it out, then add commits. After our feature has been built, we can then merge it back into the main stable branch - which is `master` by default. In this lesson we go over how to create a branch with `git branch {branch-name}`, viewing all branches with `git branch`, switching branches with `git checkout` plus a few helper commands.

Practical Git: Isolate feature development with git branch

Getting started with Git requires you to add a .git folder to your project on your machine and then set up that folder to point to a remote repository. In this lesson, we walk through using `git init` and `git remote` to do this; we use GitHub for this remote repository as an example, but keep in mind that any Git repo hosting service will work.