Learn Jest, Natural and Ruby

In this collection, we build a minimal flashcard application using React, primarily focusing on function components and the hooks API. To make sure we can understand and maintain existing code, there are also a few lessons toward the end that use class components so you can get familiar with the syntax. We cover important React concepts, and sprinkle in libraries that are pretty standard in the ecosystem like @reach/router, Jest, and React testing library. We start with create-react-app so we can get right into it without worrying about configuring tooling. There's [a separate course for that here](https://egghead.io/courses/modern-javascript-tooling-with-react?af=5mx0ie)

jest

Build a React App with the Hooks API

In this course we are going to work through properly setting up Enzyme with Jest to test rendered components. This includes the nitty gritty of making our testing environment work with various versions of React. After we get our environment setup ready, we’ll work through the different ways we can render a component within our test pages. With a rendered component we can now test component methods and properties to assert that they are what is intended. 

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

Test React Components with Enzyme and Jest

Best practices for building & testing redux applications have changed dramatically over time. This course aims to be a comprehensive and up-to-date resource for those seeking to confidently test their redux apps. Whether you're just getting started or want to improve on your existing testing strategy, there will be something in this course for you.

This first few lessons of this course look at how to add jest to an existing react/redux application that was built on top of [vite](https://vitejs.dev/). We cover basic functionality like using `npm test` vs `npx jest` and generating and using [code coverage](https://jestjs.io/docs/cli#--coverageboolean) to inform our testing strategy.

The next 10 or so lessons cover the specifics of unit testing the redux code in our application. We'll go over how to test reducers, selectors and thunks. As part of this we'll touch on mocking APIs and using [Redux Mock Store](https://github.com/reduxjs/redux-mock-store) as well as running actions through our applications' actual reducers to broaden our test coverage.

In the final part of this course we'll look at how we can test our redux connected react components using the various utilities that come with the [React Testing Library](https://testing-library.com/docs/react-testing-library/intro/). We'll cover simulating various kinds of user input with the [User Event Module](https://testing-library.com/docs/ecosystem-user-event), populating our redux store with test data, using [jest spies](https://jestjs.io/docs/jest-object#jestspyonobject-methodname), how to wait for asynchronous tasks to complete, and several approaches for finding & asserting that visual elements exist on the page.

This course is built on top of content that was created for the [Modern Redux with RTK and TypeScript](https://app.egghead.io/playlists/modern-redux-with-redux-toolkit-rtk-and-typescript-64f243c8) course and I highly recommend you start there if you haven't completed it already. It does expect a *basic* familiarity with react, redux and jest. TypeScript is used in these lessons, but is not a major focus and hopefully won't distract you too much if you prefer plain JavaScript.

No matter your experience level with redux and testing react applications, I'm confident your abilities will level up as you watch this course and follow along with the examples.

Thank you!

Confidently Testing Redux Applications with Jest & TypeScript

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

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

rails

Expanding on [Part 1](https://egghead.io/lessons/angularjs-rails-todo-api-part-1), this lesson will look at how to bootstrap AngularJS in a rails application as part of the asset pipeline. With Angular available, you'll see how to communicate with the Rails API using $resource.

Rails Todo API Part 2

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

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

Rails makes an excellent choice for delivering data to AngularJS via REST apis. In this first lesson of a two part series, you'll see how to create a simple API for CRUD operations on TODOs using Rails. This isn't an introduction to Rails, and assumes you know the basics. 

Rails Todo API Part 1

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

Jest is a painless JavaScript unit testing framework from Facebook. It is designed to be super easy to setup and is packed full of powerful features. In this lesson we see how easy it is to add to your TypeScript project. This will be followed by a quick example application.

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