Learn Ios, Natural and Serverless

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

Create a serverless application and deploy it in under thirty minutes!

We'll be using Netlify, Vite, TypeScript, and React in our project. This stack synergizes extremely well for rapidly developing professional-quality applications. 

If you haven't heard of Vite, it's the French word for quick! It's also a build tool that consists of a dev-server and an extremely fast bundler. It makes for a rapid development loop.

As for the rest. Netlify makes hosting fast and easy with serverless functions and continuous deployment. React enables you to quickly create reusable components. And TypeScript helps you write cleaner code that is easier to refactor. 

So get started and learn how to use this stack in your own applications!

Deploy a Serverless API React Application with 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

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

nativescript

Objective C is a very interesting programming language which offers a lot of power as well as interesting paradigms. Let’s look at some Objective C api’s and how they translate to NativeScript. In particular, let’s learn how to wire up iOS delegates using [Filestack's FSPickerDelegate](https://github.com/filestack/FSPicker#fspickerdelegate) as an example. We will also look at handling WeakRef’s and iterating an NSArray.

Use Objective C Delegates in NativeScript for Angular iOS 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

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

In this lesson we'll explore debugging our React Native application in an actual iOS device.

React Native: Debugging on an iOS Device

We will learn how to install an Objective C library distributed via a [CocoaPod](https://cocoapods.org) into our project and expose its native api to our app. Specifically, we will cover how to immediately start working with the library even when a [NativeScript](/q/nativescript) plugin does not exist for it. Take for example [Filestack](https://filestack.com), a really powerful file uploading api that provides an SDK written in [JavaScript](/q/javascript) for the web, but also offers native SDK's for iOS and Android. Let's take a look at how to code right against this [native iOS SDK](https://filestack.com/docs/sdks/ios).

Use an Objective C library in NativeScript for Angular iOS app

In this lesson we'll look at some simple ways to customize TabBarIOS, creating a scrollable tab area, and implementing custom icons in TabBarIOS.Item.

React Native: TabBarIOS Customization

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

Learn how to generate TypeScript declaration files for all native api’s for iOS, including any 3rd party Objective C/Swift libraries included in your project via Cocoapods or other means. This will allow you to view all the metadata for included iOS libraries to see what classes and apis are exposed to NativeScript.

Generate metadata TypeScript declarations for Objective C/Swift libraries on iOS

Building a custom NativeScript XML view component for iOS entails several interesting notes, like overriding the onLoaded method of the base NativeScript View class. Let’s look at how to properly build a custom component based on an underlying native view widget with NativeScript XML which may come from a CocoaPod. We will learn several important aspects regarding Objective C properties and methods you will need to consider. In particular, we will implement this [Objective C view component](https://github.com/Boris-Em/BEMAnalogClock)

Build a custom NativeScript view component for iOS

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.

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