Learn Natural, Serverless and Xstream

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.

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

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.

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Natural Language Processing in JavaScript with Natural

A serverless function is something that is going to allow us to do server-like things without having to manage servers, but an important distinction here is that serverless does not mean that there are **no** servers. 

The environment is actually running on top of an operating system and uses physical servers, but the responsibility for provisioning and managing the infrastructure belongs entirely to the service provider.

Some of the benefits of using serverless functions: 

- **Enhanced scalability.** No need for developers to implement code to scale. It scales automatically.
- **Reduced time to market.** Serverless is an optimized set of tools for building quickly.
- **No boiler code.** A serverless function is just a tiny piece of logic. Reducing the complexity of software.
- **More time for user experience.** Serverless functions allow teams to focus resources on the user experience and not on managing servers.
- **Security.** We can call third-party APIs securely without exposing important data on the client-side.

What Are Serverless Functions?

Netlify makes deploying to production a breeze by configuring your Netlify CLI to your Netlify account and integrating with GitHub.

We'll log in to Netlify through the CLI and initialize a site by completing all the options that the CLI runs us through. Once this is done, Netlify will trigger new builds for your site every time you push to GitHub.

Deploy Serverless Functions to Production on Netlify using the Netlify CLI

Netlify makes developing serverless functions easy with the netlify-cli (`ntl` for short). You'll be able to build and test functions locally as well as publish your functions from the CLI. 

We will install netlify-cli globally and create a `netlify.toml` file that will configure where the CLI should look to run functions that we define, in our case `functions/`. When the application is served up, Netlify runs functions under `/.netlify/functions/`.

Set up a Local Development Environment for Serverless Functions Using Netlify

There are a few ways you can run Netlify serverless functions while you are developing them. 

You can: 

- Navigate to the directory under the `/.netlify/functions/` path
- Curl them using the full URL
- Use an app like Postman

Manually Trigger Netlify Serverless Functions Locally

CORS limits websites from communicating with other domains without the full consent of both sites. Consuming data from a 3rd Party REST API makes it difficult since we can't properly configure the appropriate CORS headers. To solve this, we'll set up a proxy server to request the data using a Netlify function that avoids CORS altogether.

The Third-Party API that we'll be working with will provide the following data: `id`, `name`, `favoriteSong` for each corgi. 

[http://no-cors-api.netlify.app/api/corgis](http://no-cors-api.netlify.app/api/corgis)

We will also use `node-fetch` a light-weight module that brings the `fetch` API to fetch the data into our application.

Circumvent CORS when Accessing a Third-Party API using Netlify Functions 

Environment variables set in a `.env` file can be imported using the Netlify CLI with the following command: `netlify env:import`.

This allows you to manage environment variables directly on Netlify, providing the benefit of security and flexibility of sharing environment variables with your team. 

From inside Netlify, you can access the environment variables in:

`Site settings` > `Build & deploy` > `Environment` > `Environment variables`

Import and Set Environment Variables from a .env file using Netlify CLI

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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In this video you'll learn how to deploy a React app to AWS using AWS Amplify Hosting's drag and drop folder functionality. The app will also automatically be globally distributed on AWS's global CDN.

Deploy a React App to AWS in 24 seconds

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

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

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream.

cycle

This lesson is the first in an introduction to Cycle.js. We will see how to build from scratch a toy version of Cycle.js. See how to use RxJS to create simple web apps, and how to separate logic from effects.

Separate Logic from Effects in Cycle.js

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike using Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

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