Learn Natural, Ramda and Webstorm

Learn how you can use ramda.js to bring functional programming concepts into your JavaScript code. Ramda offers composability and immutability right out of the box, so you can leave your imperative code behind and build cleaner, more maintainable code.

A library designed specifically for a functional programming style, one that makes it easy to create functional pipelines, one that never mutates user data.

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Functional Programming in JavaScript with Ramda.js

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

In this lesson we'll see how to use the `without` function to remove items in a known list from an array. Then we'll generate our exclusion list and apply it to `without` using `reduce`, `min`, `max` and `converge` to detect the highest and lowest values in an array and creating a new array that excludes those values.

Discard the High and Low Values From an Array of Numbers with Ramda

In this lesson we'll look at how you can use Ramda's `unfold` function to generate a list of values based on an initial seed.

Create an Array From a Seed Value with Ramda's unfold

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

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.

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When exploring a new framework or technology, it is nice to have a seed project or sandbox so you can just get in and start playing. Let's build our React playground using npm, browserify, 6to5, and more within WebStorm!

Setting up a React Playground Dev Environment

Functional composition is a great way to write declarative code that reads well. But sometimes, you need to get insight into what's happening at different steps in your composition. This is where `tap` comes in. With tap, we can run code that generates side-effects, like logging to the console, and our data will continue to be passed along, untouched.

Debug Function Compositions with Ramda's Tap Function

Most of the functions offered by the ramda library are curried by default. Functions you've created or that you've pulled in from another library may not be curried. Ramda's `curry` and `curryN` functions allow you to take a non-curried function and use it as a curried functions. In the case where you have a manually curried function and you want to just call it like a normal function, you can use `uncurryN` to get back a function that accepts all of the arguments at once.

Curry and Uncurry Functions with Ramda

You can really unlock the power of ramda (and functional programming in general) when you combine functions. Counting words in a string may seem like a relatively difficult task, but ramda makes it easy by providing a `countBy` function.  This lesson walks through using the `countBy` to count words in a string.

Count Words in a String with Ramda's countBy and invert

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 refactor a React component to use Ramda lenses to update our component state. We'll create a lens to focus on the property we want to target and use `over` to apply the existing state value to a utility function and we'll get back a new state that will be reflected in our rendered UI.

Update Component State in React With Ramda Lenses

Using the map method of an array is a great way to create new data from an existing array in an immutable way and without writing any loops. The problem is that you need to call it on an existing array instance. In the lesson we'll see how to build our own reusable `map` utility function using manual currying and then we'll replace it with Ramda's `map`.

Create a Reusable Mapping Function with Ramda's Map

In this lesson, we'll use `Promise.all` to get an array that contains the resolved values from multiple promises. Then we'll see how we can use Ramda to convert that array of values into a single object using `zip` with `fromPairs`. Then we'll refactor to use `zipObj`.

Convert a Promise.all Result to an Object with Ramda's zip and zipObj

We don't always control the data we need in our applications, and that means we often find ourselves massaging and transforming our data. In this lesson, we'll learn how to transform objects in a declarative way using ramda's evolve function.

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