Learn Natural, Stripe and Tweenmax

### Accept payments & sell products powered by Stripe and the best of the JAMStack

There are as many ways to build an e-commerce store on the internet as there are products to sell. One thing is for certain, e-commerce is here to stay and as professional developers we need to understand how to build fully custom stores for our clients using the best modern tools available.

- React: flexible and customizable while following modern best practices
- Next.js: lightening fast with guide rails to help your project perform as consumers expect
- Stripe Checkout: lets you offload reams of complicated business logic to a trusted third party that maintains regulatory compliance, global payments, and a standard UX.

Your store will have well managed local component state using React Hooks and you'll also have clear and cohesive shared (global) state with React Context.

Finally you'll deploy your custom store to Vercel (the platform behind Next.js) as well as how to make your Next.js e-commerce store portable to deploy to other platforms.

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

Create an eCommerce Store with Next.js and Stripe Checkout

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

Payments infrastructure for the internet.

stripe

Stripe has a variety of solutions that makes it super powerful as a payment provider. We're taking advantage of their Stripe Checkout product because it allows us to get started with an online store without having to manually accept payments.

To do this though, Stripe Checkout relies on the browser to make API requests, which we have to configure to work for our website.

We will set our eCommerce site domain on Stripe's CLIENT-ONLY integration to set us up to add the checkout workflow for our site.

Configure a Stripe Checkout Domain for Client-Only Integration

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

With Stripe or many other services, their APIs rely on special keys that authenticate requests or allow that service to track usage for things like billing.

Some of these keys are able to be used publicly and some must be kept private, but ultimately we want to have a way to easily manage these keys and not hard-code them into our application, which is where environment variables come in.

We will find our Stripe public key that will be used locally as well as in production on Vercel. You'll learn how to set an environment variable in Next.js (hint, you need to prepend `NEXT_PUBLIC` to the variable name make it available in the client) as well as in production on Vercel.

Add a Stripe API Key as an Environment Variable in Next.js & Vercel

Now that we have an app and our products configure in Stripe, it's time to connect the two and give people a way to purchase our products.

Using Stripe's API, we can initiate a new instance of the Stripe Checkout flow with a simple click of a button.

We'll create an `initiateCheckout` function that handles this flow for us. After loading stripe through `@stripe/stripe-js` we'll `await` a `stripePromise` that will allow us to redirect to stripes checkout flow. You'll notice that we will need to pass in line items with the price id and quantity.

Integrate Stripe Checkout to Purchase Products in Next.js with Stripe @stripe/stripe-js Cl

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

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

TimelineLite is a piece of the Greensock TweenMax library that provides the ability to create sequenced animation with very little code or setup.

Greensock TimelineLite Animation Sequences

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

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.

Find a word’s definition using WordNet in Natural

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

Using machine learning classifiers in a new project

A part of Natural Language Processing (NLP) is processing text by “tokenizing” language strings. This means we can break up a string of text into parts by word, sentence, etc. In this lesson, we will use the `natural` library to tokenize a string. First, we will break the string into words using `WordTokenizer`, `WordPunctTokenizer`, and `TreebankWordTokenizer`. Then we will break the string into sentences using `RegexpTokenizer`.

Break up language strings into parts using Natural

[Greensock](http://www.greensock.com/tweenmax/) is a powerful animation library for JavaScript. It's also fully compatible with AngularJS. This lesson will get you started using Greensock with AngularJS to provide rich animations for your users.

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