Learn Python, Reason and Tachyons

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

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Fully Connected Neural Networks with Keras

This course foremost will teach you about the syntax and semantics of the programming language Reason or also referred to as ReasonML. In addition, the course touches on best practices recommended by the official documentation. Each lesson is focusing on a specific feature, concept or best practice and can be watched independently. Still, the whole course is carefully crafted, and each lesson builds up on the previous ones.

Simple, fast & type safe code that leverages the JavaScript & OCaml ecosystems.

reason

Get Started with Reason

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

scikit-learn

Introductory Machine Learning Algorithms in Python with scikit-learn

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

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.

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Up and Running with AWS Amplify Functions

Node.js is a well established server runtime that many companies are using worldwide. Express.js is a server framework for Node.js used by over 5 million projects. ReasonML is a type-safe functional language that compiles to Javascript.

This playlist collects the basic pieces of information a programmer needs to get started building servers in ReasonML on Node.js and Express.js. Follow along to learn how to get the speed of Node, combined with the added type-safety of Reason.

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

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Making an HTTP server in ReasonML on top of Node.js

Ongoing playlist.

Don't be afraid to watch videos at 1.25x if the pace is too slow!

ReasonML Applications for the React Developer

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Sometimes you get strings instead of objects and you have to parse them out. Here's an example where the raw data I got from a Twitch chatbot message wasn't useful to me until I parsed it into a dictionary.

Turning a key/value string into a dictionary in Python

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

Throughout the last year, the use of React hooks has taken the world of web development. Hooks provide users with an easy way to handle interactivity within ReactJS applications, so it's no surprise that the latest release of the ReasonReact library makes great use of them. 

In this lesson, we'll get started using ReasonReact `0.7.x` by scaffolding out an application using the [BuckleScript](https://bucklescript.github.io/) command-line tool. Once scaffolded, we'll go through the steps needed in order to properly run our application within a development environment that is ripe for productivity!

### Resources
* [ReasonML Docs](https://reasonml.github.io/docs/)
* [ReasoNReact Docs](https://reasonml.github.io/reason-react/en/)
* [BuckleScript Docs](https://bucklescript.github.io/)

Up and Running with React Hooks inside of a ReasonML 

Any React developer will agree that 2019 was the year of that hooks took the library by storm. Luckily for us, the ReasonRect library makes it possible to hooks within the context of a ReasonML application.

In this lesson we will take a test drive of the `useReducer` hook within a ReasonML application. By the end, we'll have learned how to define our actions using variants, use records to shape our state, and more!

### Resources
* [ReasonReact Docs] (https://reasonml.github.io/reason-react/en/)
* [ReasonReact Blog - Support for hooks in 0.7.0 Release](https://reasonml.github.io/reason-react/blog/2019/04/10/react-hooks)



Use ReasonMLs Variant & Record types with ReasonReact's useReducer hook to Manage State

When working with traditional React, we often pass functional stateless components the `props` object as an argument. Although there are a few minor differences when working in the context of Reason, the act of passing `props` to a component is done in an extremely similar manner.

In this lesson, we will get a first-hand look at just how similar these processes are by walking through the steps of creating a React Component in ReasonReact. By the end, you'll have developed an understanding of ReasonReact's "named args" syntax which it uses for creating components and you'll have created a re-usable Layout component that can be used to share layout throughout an application.

### Documentation
* [ReasonReact Homepage](https://reasonml.github.io/reason-react/)
* [ReasonReact Docs - Components](https://reasonml.github.io/reason-react/docs/en/components)


Create a Layout Component for Displaying Child Components Inside of a ReasonReact App

One of the key benefits of BuckleScript is the ease in which it makes the process of porting JavaScript libraries so they can be used within the context of a ReasonML application. The Reason Package Index, or [redex](https://redex.github.io/), provides a central place for browsing all available packages. Additionally, creating your own BuckleScript bindings is a straight forward process that also provides value in the form of knowledge surrounding the inner-workings of the library at hand.

In this lesson, we learn how to integrate the emotion css-in-js libray into our project by installing the `bs-css` package. Once installed, we'll create a `.re` file for our style rules and see how we can add global css, as well as styles for specific css classes which we'll target using React's `className` prop.

### Link
* [ReasonReact Docs](https://reasonml.github.io/reason-react/docs)
* [`bs-css` - GitHub](https://github.com/SentiaAnalytics/bs-css)
* [`bs-css` - Redex](https://redex.github.io/package/bs-css)


Implement a Sticky Footer within a ReasonReact App Using the bs-css BuckleScript Package

ReasonML is a fantastic way to write Javascript! But it doesn't yet have a built-in async / await syntax. Furthermore the built-in libraries for working with Javascript promises are awkward to work with, and produce code that's hard to read. This lesson will teach the viewer how to use the "bs-let" language extension (called a PPX) to achieve readable, easy to write promise-based code.

Async / Await in ReasonML

Although ReasonML has built-in libraries for working with JSON, they are low-level and difficult to use by hand. The Decco library offers automatically generated JSON decoders, and produce a result that keeps the programmer safe at runtime by telling them whether the data they got matched the shape of the data they expected. 

Automatically decode JSON into records in ReasonML using Decco

This lesson shows how the Serbet server library for ReasonML and Node.js makes handling JSON request bodies, and responding with JSON request bodies a simple task.

Handle JSON request bodies and send JSON responses from a Node.js server using ReasonML

Decco is a great library for automatically generating JSON encoders and decoders, but most real-world projects will quickly run into the need to add custom data types that Decco doesn't ship with support for. This lesson shows how to simply add a custom data type by implementing a codec that reads and writes Javascript dates as floats.

Write a simple custom JSON decoder for Decco in ReasonML

Decco is a language extension that automatically generates JSON encoders and decoders from type signatures. When adding adding custom Decoders, the error messages that the compiler produces can be quite confusing. This lesson shows the viewer how to use explicit type signatures to narrow down where an error might be coming from in custom codec code. This same pattern can be applied whenever confusing error messages come from code that is written without explicit type signatures.

Working with cryptic error messages from ReasonML when writing custom Decco decoders.

This lesson shows how to use the `serbet` npm package to write a simple Node.js http server in ReasonML by implementing a simple "hello world" endpoint. Serbet is a convenient wrapper around Express.js. It's a young library, but its patterns are already used in production code.

Make a basic server with ReasonML and Node.js

The BuckleScript compiler recently reached a new milestone with version 7. In this lesson, we walk through the process of upgrading an existing application to the latest version using `yarn` (or `npm`).


### Resources

[BuckleScript Blog - What's new in v7](https://bucklescript.github.io/blog/2019/11/18/whats-new-in-7)

[BuckleScript Blog - Holiday Release Post](https://bucklescript.github.io/blog/2019/12/20/release-7-02)

!![BuckleScript Docs](https://bucklescript.github.io/docs/)

Migrate an Existing BuckleScript Project to bs-platform v7

Tailwind is a utility-first CSS framework for rapidly building custom user interfaces.

tailwind

This lessons shows how to dynamically add a red border to an input when a validation error pops occurs using ReasonReact, Tailwind, and re-classnames.

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