Learn Grep, Natural and Rust

In this course, Chris takes a README-driven approach to build out a Rust CLI that supports content authoring for a digital garden.

Specifically, you'll work through the first command for the CLI, writing files. You start by initializing a new Cargo binary crate through creating the `write` command that allows you to create files (and their contents) to the folder that you set. 

Step by step you'll learn about:
- Handling user input
- Error handling
- Rust Move semantics
- Rust Copy semantics
- Integration testing

...and more!

Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.

rust

Creating a Digital Garden CLI with Rust

[Rustlings](https://github.com/rust-lang/rustlings) is a set of exercises that serves as a great introduction to the Rust language. It covers many concepts from the [Rust book](https://doc.rust-lang.org/book/) by asking you to work through sets of exercises.

This collection walks you through Rustlings in a way that explains what's going on in each exercise. We cover everything from basic types to generics and threading.

Learning Rust by Working Through the Rustlings Exercises

Rust is a modern language that is close to the metal. It's got the performance of C, but with a more friendly syntax and community.

It's also memory safe, and ensures your applications are bug free by catching errors at compile time instead of runtime.

These features make Rust a great choice for developing code for embedded systems, web servers, and CLI apps. You can even compile Rust into WebAssembly in order to run it in-browser!

But as the saying goes, "before you learn to run, you must learn to walk".

In this course, you will write your first Rust application.

Starting from “Hello World” you’ll quickly move on to learning about Rust’s types, configuration files, and the `cargo` package manager along with classics like functions, loops, and handling user input.

More Rust-specific concepts like macros and pattern matching will also be introduced.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/write-your-first-program-with-the-rust-language).

Next Steps: [Basic Types in Rust](https://egghead.io/playlists/data-types-in-rust-ab72)

Write Your First Program with the Rust Language

This collection contains videos that explore the basic types in Rust, including **Fixed size Integer**, **Pointer sized Integer**, **Boolean**, **Strings**, **Characters**, **Arrays**, **Tuples** and more.

If you're just starting out learning Rust and [wrote your first program](https://egghead.io/courses/write-your-first-program-with-the-rust-language), this collection is the right place to get more familiar with the language!

Basic Types in Rust

Many languages work on a compilation story to WebAssembly, but Rust is already ready for primetime. Why?


- Rust is a stable language and great replacement for C and C++. 
- Due to the reference and borrowing concept, Rust doesn’t need a Garbage Collector and can be used for a production-ready WebAssembly project. 
- The Rust/Wasm team has built some sophisticated tooling for straightforward interoperability between Rust and JavaScript.

This course gives you the tools to compile to WebAssembly with Rust. Over 11 lessons, we’ll set up a project and explore various paths of communication and interoperability between Rust and JavaScript.

Using WebAssembly with Rust

CMD+F in your text editor can only get you so far! In this course, we will learn to use grep and find, two powerful command-line utilities, to search through and explore our codebase. Learn to search specific files and folders, using regular expressions to look for -- or ignore! -- particular phrases. We'll cover how to search recursively, get more context on your results, and some simple tricks to make your searches faster and more targeted.


Once you're familiar with grep and find, you'll be able to quickly answer questions such as: Do I use this function anywhere other than my tests? Where does this file get imported? Whether you're working on a new feature, reading a codebase for the first time, or refactoring existing code, grep and find can help you work faster and more effectively.

A Unix command used to search files for the occurrence of a string of characters that matches a specified pattern.

grep

Use Grep for Fast Search from the Command Line

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

Sometimes you'll be looking for a string, but won't know which file it's in; or you'll want to find all usages of it within a directory. Learn how to use grep -r to recursively search all the files in a directory. 

Search directory contents recursively using grep

Learn how to use find to identify filenames matching specified patterns. We'll use find to identify all .jsx files in a directory, or all test files in a directory.

Use find to search for filename patterns

Learn to use git grep to only search through the tracked files in a git repo. This is especially useful when you want to exclude build artifacts or locally installed dependencies, such as webpack bundles or the node_modules directory. You'll also note that git grep is automatically colorized - we'll see how to get the same coloring effect with grep --color.

Search the contents of a git repository with git grep

By default, grep displays the file path and the line where a match is found. Learn to use the flags -A, -B, -C, and -n to get more context, and view line numbers as well as the lines surrounding a pattern match.


Show context around matches using grep

By using wildcards and glob patterns with the special characters . and *, you can create regular expressions that describe what kind of text you're looking for, instead of just plaintext strings. 

Search for basic patterns using grep

Learn to use grep's extended regular expressions to describe more complex patterns. The `?` and `+` special characters describe optional patterns. The `?` character matches zero or one instance of the preceding term, and the `+` character matches one or more instances of the preceding term. To use these characters, you'll need to either escape them with backslashes or turn on extended regular expressions with the `-E` flag.

Use grep's extended regular expressions to find complex patterns

Learn to use the special anchor characters `^` and `$` to indicate the beginning and end of lines when writing regular expressions for grep. These line anchors are part of basic regular expressions in grep, so you don't need to escape them.