Learn Flux, Natural and Twit

In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

Twitter API Client for node

Supports both the REST and Streaming API.

twit

Create your own twitter bots

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

We’ll get a real-time stream of tweets, and learn how to filter a stream by keyword, location, and user.

Use the Twitter Stream API with Twit.js

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

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

Actions contain no functionality, but rather describe an event in our application.  In this lesson we will describe our Actions as they relate to our application.  These Actions will then be available for triggering in our Views and for execution in our Store.

React Flux: Actions

In Flux our Components are referred to as Views, but they are the same familiar Components associated with any React development.  In Flux, however, we have the benefit of the architecture to keep us from having to pass down through a long chain of children Components any functionality that may be embedded in our Stores.  In this Lesson we will wire up all of our Components in record time utilizing the architecture we've already established.

React Flux: Components/Views

Stores are where the real work in our application is done.  Dispatchers broadcast Actions to all Stores, and the Stores registered to listen for those Actions will perform any logic needed to update our Views.  In this lesson we will establish our first Store and register the Actions we wish to respond to.

React Flux: Stores

Our build process during this series will utilize both gulp and browserify.  In this lesson we will take a moment to setup our environment and all of the tooling required to begin developing our Flux Application.  We've kept the build process very simple so porting it to grunt should be a breeze, but this also serves as a simple primer for those who may not be familiar with gulp.

React Flux: Development Environment Setup

Organizing modules and components is a critical aspect of the development of any single-page web application (SPA). In this lesson we will look at refactoring the Flux React store into a more module structure.

React Flux: Project Organization

Flux has four major components: Stores, Dispatchers, Views, and Actions.  These components interact less like a typical MVC and more like an Event Bus.  In this lesson we will start by creating our Dispatcher which queues up our Actions as Promises and executes them as they are received.  We'll also create an AppDispatcher which, unlike the Dispatcher, is more specific to our application.

React Flux: Overview and Dispatchers

As your React Flux application grows, it is inevitable that you start to see functionality and code repeated. With React, you can easily keep things DRY by using mixins.

React Flux: Remove Duplicate Code with Mixins

With this lesson, we will bring it all together, using what we've learned in the series to fisnish out the React Flux architecture example.

The completed code for this series can be found [here on Github](https://github.com/eggheadio/egghead-react-flux-example).

React Flux: Wrapping Up

By default, React doesn't have built in routing. We will add routing to our application with the react-routing-component.