Learn Angular-cli, Natural and Particle

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 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

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

angular-cli

Generating components with the Angular CLI allows for scaffolding applications quickly. Learn what will be generated for you and how to generate quickly with shortcuts in the desired directory.

Generate components with the Angular CLI

Angular is a development platform for creating applications using modern web standards. Angular includes a wealth of essential features such as mobile gestures, animations, filtering, routing, data binding, security, internationalization, and beautiful UI components. It's extremely modular, lightweight, and easy to learn.

angular

Getting started with angular development can be difficult, with a lot of moving parts. Allow the Angular CLI to handle those complexities so you can focus on creating great applications.

In this lesson you will learn how to install the Angular CLI from npm, create a new application, serve the new application in the browser and edit it with live-reload.

Install the Angular CLI to Create and Serve an Angular Application

In this lesson we will our `UiModule`, that has a `LayoutComponent`, `HeaderComponent` and `FooterComponent`. 

We generate `UiModule` by running the following command:
```
ng generate module ui --module app
```

This generates the new module and imports it in our `AppModule`.

We'll create the `LayoutComponent` using:
```
ng generate component ui/containers/layout
ng generate component ui/containers/header
ng generate component ui/containers/footer
``` 

We update our `AppRoutingModule` and add this component to our first route to make it visible. 

The `HeaderComponent` and `FooterComponent` are referenced in the LayoutComponent and we implement the actual templates!

Create Application Layout with Components in an Angular CLI Project

In this lesson we will add the `ProductModule`, where our actual application code will live.

```
ng g m product --routing
``` 

We will add 2 routes to `AppRoutingModule`,  the first is a redirect route that redirects any unhandled paths to `products`. The second route is the actual `products` route, that 'lazy-loads' the `ProductModule`.

We use the `ng` command to create the `ProductListComponent` and `ProductDetailComponent`, which are so called 'containers components'.

Container components are responsible for fetching data, handling routes and passing data into the 'presentational components' 

We will add 2 routes to `ProductRoutingModule`,  one to each of our new Container Components.

Lastly we will use  `ActivatedRoute` in `ProductDetailComponent` to fetch the `:id` from the route.

Create Container Components for Retrieving Data in an Angular CLI Project

In this lesson we will create the presentational components for the product list and detail pages. These components define how the data is displayed and are being referenced from the Container components.

The new components are `ProductsComponent` and `ProductComponent`.

After using `ng generate` to generate these components, we will update the `ProductListComponent` to invoke `ProductsComponent` and pass in our local `products` array.

In `ProductsComponent` we loop over each of the items we get from the `@Input() products` and pass each one into our `ProductComponent`.

The `ProductComponent` has 2 `Inputs()`, one for the `Product` and a `boolean` that toggles showing the product details. This is useful because don't want to show product details in the list view.

After the `ProductComponent` is implemented the last step is to reference it from the `ProductDetails` container component. 

Create Presentational Components for Displaying Data in an Angular CLI Project

In this lesson we will create the `ProductsService` that is responsible for the interaction with our API. 

We will inject the `HttpClient` that we get from `@angular/common/http`. To make this working we need to import `HttpClientModule` in our `AppModule`.

We define a `getProducts` and `getProduct` method so we can get a list of products and an single product which we will use in respectively the `ProductList` and `ProductDetail` components. 

In both components we will use the `ngOnInit` lifecycle hook to invoke the methods on the service, and retrieve the data by subscribing to it. 

[Link to product api used in lessons](https://github.com/beeman/egghead-products-api)

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