Learn Egghead, Natural and Twit

Miscellaneous one-off lessons on debugging, productivity hacks, and JavaScript tricks.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Tips and Tricks

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

This is the video companion to the [egghead.io Instructor Guide](https://howtoegghead.com/instructor/).

Screencasting is hard! There are virtually infinite ways to produce one. For an egghead.io lesson we expect several key aspects to be met. We want to make sure that our lessons have depth and clarity. Clarity is important and starts with the technical aspects of recording your screen and voice. Once we have achieved this objective level of clarity, we dip into the stylistic aspects of recording a lesson. The style is subjective, and the goal of an egghead.io lesson is not to have an army of uniform drone producing flavorless video content for the masses.

Our mission is to make you a badass screencaster. We want to push you to another level. We've found that a small set of standard practices will create an awesome experience for the student, and eliminate frustration as we teach by providing a repeatable pattern for success. It’s not complicated. In fact, it is simple, but it isn’t easy.


This course will show you how to produce an egghead.io lesson. We strive for consistency in style, and these lessons will demonstrate the core elements that create our brand screencasts. Let’s learn how together.

Bite-size video tutorials for badass web developers.

egghead

Record Badass Screencasts for egghead.io

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

Any extra information on the screen will easily distract the audience from the purpose of your lesson. Do your best to remove any extra notifications, UI, or other information that would take away from what you're teaching.

Remove Distractions

Any information you would want to put into an Intro or Outro belongs in the accompanying description, title, course page, etc. The lesson is only for showing the process. Talking about yourself, the lesson, or other lessons is a waste of time.

Avoid Intros and Outros

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

Tangents are opportunities for other lessons. When you find yourself drifting off-topic because you feel you _need_ to explain  some code you wrote, consider how that piece of code fits back into a "How do I?" question, then come up with a separate lesson to capture it.

Stay on Topic

Recording can be intimidating, so it's best to just hit the record button and start talking. Take it one thought at a time. Sit quietly, think about what you're going to say/type, then do it. If you mess, up, undo back to when you were quiet and try again. Once you get a good take, move on to the next thought. The ideal raw recording is a ton of bad takes and one good take for each thought. So when you get to the editing phase, you quickly clear out the bad takes and leave the good ones.

Record One Thought at a Time

Editing a raw lesson video is a very simple process. You simply watch your own lesson and edit out all the bad takes and dead space using the "Ripple Delete" command. You will be left with a nice, cut lesson ready to export and upload.

Edit with Ripple Delete

Video is a one-way form of communication, so user's don't have the chance to ask you to clarify or ask "Where does X fit in?" This places the burden on you to guide their eyes as you explain how all the little pieces of the code fit together. When your application has a "flow", "X triggers Y, Y triggers Z", make sure to "show the flow" by walking them through where each chunk of code fits. This involves a lot of mouse movement (or fancy keyboard selection shortcuts) and hightlighting to bring it all together.

Guide Their Eyes

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

Code is usually pretty good at explaining itself. So it's best to just start writing code while you talking about it, then see if any additional explanation is required. When you fall into the trap of a long-winded explanation before you begin coding, you risk confusing the audience or unnecessarily repeating yourself.