Learn Android, Natural and Screen-reader

A large number of people are unable to use the web effectively due to an impairment or disability.

As developers there are several tools and techniques we can use to make our web applications accessible, ensuring a great user experience that includes everyone.

We all agree that Accessibility is important. However, it’s a broad landscape and can be overwhelming figuring out where to start!

Erin Doyle is an expert in creating accessible React applications and has developed a course that will give you a concrete process for testing, refactoring, and building your applications with accessibility in mind.

After this course, you'll have a jump start on auditing and fixing accessibility issues in your applications and gain a better understanding of your target users and how to approach your web app design from their perspectives.


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

Develop Accessible Web Apps with React

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

Open source framework for building truly native mobile apps with Angular, TypeScript or JavaScript.

nativescript

We will learn how to add an Android library to our project and translate it’s Java api to NativeScript. For this lesson, we will look at [Filestack’s Android SDK](https://github.com/filestack/filepicker-android) built with Gradle. Specifically, we will cover how to immediately start working with the library even when a [NativeScript](/q/nativescript) plugin does not exist for it.

Use an Android library in NativeScript for Angular using Java apis

Java has been around a long time and when it comes to Android, you will encounter some structures that may throw you for a loop on how to code against with NativeScript. Let’s examine a few common Java/Android api constructs you may encounter and how to translate them to NativeScript. Learn how to get references to local files in your app using the ‘file-system’ module as well as how to take advantage of NativeScript’s Observable class to send notification events from one class to another. Lastly, look at considerations with Angular’s NgZone when handling your native libraries data events to update component view bindings.

Translate common Java/Android APIs into NativeScript for Angular Android apps

One of the exciting benefits you have with NativeScript is the ability to write native code (Java and Objective C) directly in JavaScript/TypeScript allowing you to create a consistent high level api over top of both underlying iOS Objective C/Swift and Android Java apis. In this lesson, we will look at how to design a consistent api that taps into the appropriate native api’s on the target platforms using underlying native libraries.

Develop a consistent plugin api across your Android and iOS NativeScript app

To prepare your development environment to code against native apis such as Objective C, Swift and Java, let's learn how to [install tns-platform-declarations](https://www.npmjs.com/package/tns-platform-declarations) to provide rich intellisense support for all the native iOS and Android api’s. This lesson will cover how to modify your tsconfig.json and references.d.ts properly to ensure working with native api’s is smooth and as helpful as possible.

Prepare for native iOS and Android api intellisense with NativeScript

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

Building a custom NativeScript XML view component for Android is uniquely different than it’s iOS counterpart because of the Android Context which must be considered. Let’s look at how to properly build a custom view component based on an underlying native view widget. We will also look at what happens when our include.gradle file is not setup properly including handling problems with minimum sdk targets with plugins. Specifically, we will implement this Android widget representing the [Google I/O 2016 clock](https://github.com/lypeer/GoogleClock). Learn how to create a custom AndroidManifest.xml inside your plugin to merge with your app's manifest to override certain characteristics. Read detailed documentation about the [Android app manifest file](https://developer.android.com/guide/topics/manifest/manifest-intro.html).

Build a custom NativeScript view component for Android

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

screen-reader

There are many techniques for hiding content in user interfaces, and not all are created equal! Learn how different hiding techniques in HTML, CSS and ARIA impact keyboard and screen reader users in addition to visual display. As a bonus, we'll also take a look using a screen reader on a mobile device.

How Visible vs. Hidden Elements Affect Keyboard/Screen Reader Users

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.

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