Learn 11ty, Natural and Particle

Eleventy, or 11ty,  is a simple yet very powerful static site generator. It does not require any config to get started.

This what it will take you to get an 11ty project running.

```
npm install -g @11ty/eleventy
echo '# Page header' > README.md
eleventy
```

After watching this collection you will be able to:

* Bootstrap a simple 11ty website
* Use 11ty layouts
* Chaining multiple 11ty layouts
* Use 11ty Collections
* Create pages from data

...and much more

Eleventy was created to be a JavaScript alternative to Jekyll. It’s zero-config by default but has flexible configuration options. Eleventy works with your project’s existing directory structure.

Eleventy uses independent template engines. We don’t want to hold your content hostage. If you decide to use something else later, having your content decoupled in this way will make migration easier.

11ty

Getting Started with Eleventy

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

Start from a blank project and build up to an Eleventy site that includes a blog collection and is prepared to source content from a headless CMS. You'll also learn how to add Sass as the styling solution, complete with triggering 11ty to re-compile when the Sass changes.

These lessons will help explain key 11ty features and concepts to help you make the most of this amazing static site generator.

By the last lesson, you'll have learned how to:
- define custom `input` and `output` directories
- create a layout that accesses front matter variables
- watch for Sass changes and include CSS in the build proces
- create an 11ty collection using `tags`
- define front matter for an entire collection
- make and apply a filter to a collection
- generate pages from an external data source, such as a CMS or API

Build An Eleventy (11ty) Site From Scratch

In this video, you will learn to bootstrap an eleventy project.

we'll start from an empty directory, initialize it as an npm package, install `@11ty/eleventy` package and create the website entry point.

Eleventy can compile multiple file formats, HTML, markdown, liquid, and njk.

Check out the eleventy [docs](https://www.11ty.dev/docs/) for more information.

Bootstrap an eleventy project

In this video, you will learn how to use the eleventy layouts feature.
Eleventy Layouts are special templates that can be used to wrap other content.


for more information check the layouts [docs](https://www.11ty.dev/docs/layouts/)

Use eleventy layouts

In this video, you will learn how to use layout chaining with eleventy.
Layouts in eleventy can use other layouts. You can also use a different template engine across layouts. This is very handy if we want to wrap some pages with extra markup but not all the pages.


for more information check out the eleventy [docs](https://www.11ty.dev/docs/layout-chaining/)

Use Layout Chaining in eleventy

In this video, you will learn how to create pages from data in eleventy. a data source can be a JSON file or a js file.

for more information check out the eleventy [docs](https://www.11ty.dev/docs/pages-from-data/)

Create pages from data in eleventy

In this video, you will learn how to use the collection feature in eleventy.
Collections will allow to group content under one tag and loop through it in your template files

for more information check out the eleventy [docs](https://www.11ty.dev/docs/collections/)

Use Collections in eleventy

Begining from a brand new project, learn how to install Eleventy (11ty), and add `develop` and `build` scripts. Next, define a custom configuration to set preferred input and output directories. Then create a home page file and your first layout while learning how 11ty handles mixing templating languages.

Create an Eleventy (11ty) Site From Scratch

Learn how to add Sass to an Eleventy (11ty) project including directing 11ty to re-compile when Sass changes are made. This step involves adding two new functions to the `.eleventy.js` configuration: `addWatchTarget()` and `addPassthroughCopy()`. Then, define the link to the compiled CSS file within an 11ty layout.

Add Sass Compiling and Watch for Changes in Eleventy (11ty)

Create a blog directory with posts for your Eleventy (11ty) project, and learn how to define the posts as an 11ty collection using `tags`. Then make a blog index page and write a `for` loop with Nunjucks to output a list of each post linked to the post page.

Define an Eleventy (11ty) Collection to Create a Blog

Filters in Eleventy (11ty) are a way to mutate data including string variables or arrays such as collections. Learn to create a filter for use on a collection which will select a random item from the collection.

Apply a Filter to an Eleventy (11ty) Collection

Eleventy (11ty) is able to run any JavaScript or Node functions at build time. This makes it possible to source external data such as from a headless CMS or other API. Learn how to generate pages from data pulled from a mock CMS using the 11ty `pagination` method and designate the data as an 11ty collection. You'll also learn how to define variables for the page using `eleventyComputed`, and then write a `for` loop to output a link to each page.

Generate Eleventy (11ty) Pages From External Data

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. 

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