Learn Elm, Python and React-storybook

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

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

elm-ui is a novel iteration on declarative styling where you can use Elm types and functions to define your UI in a declarative way. The novel part is that it does not require you to know CSS to be used, you can start building layouts right away!

You'll learn how to:
- use a declarative mindset for building UIs
- position elements on the screen
- create layouts with rows and columns
- build a form with elm-ui

Elm is a programming language that provides a clean functional syntax for writing HTML web applications. It promises zero runtime exceptions, fast rendering, time-travel debugging, and much more.

Declarative UIs without CSS with elm-ui

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

Elm is a beginner friendly functional reactive programming language for building web applications. It contains a small but powerful set of language constructs, such as, static types, algebraic data types, immutability, and pure functions. Elm provides and requires the use of a specific architecture that’s well suited for sensible application construction. Elm’s type system and architecture may feel constraining at first, but they ultimately provide the programmer with some neat benefits including comprehensible code, and a much improved refactoring experience.

In this course you will learn the basic fundamentals of Elm so that you can start building applications today.

Start Using Elm to Build Web Applications

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

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

Storybook is a UI component development environment for React, Vue, and Angular. With that, you can develop UI components without running your app.

Here I show how best to configure a React Storybook to use [Typescript](/q/typescript) (with TSX). This lets you write your reusable components with maximal type safety. Please see the provided code for the full source.

Configure React Storybook for use with Typescript

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

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

Read from a File in Python

Learn how to create a tuple in Python. A tuple is an immutable data type that contains several items similar to a list. Items in a tuple can be of any data type.

Create a Tuple in Python

Learn to create a one item tuple. When creating a one item tuple, a comma has to be included after the item. Otherwise, your tuple will not return tuple as its data type. 

Create a One Item Tuple in Python

[Storybook](https://storybook.js.org/) is a framework that allows you to develop components in isolation. It is ideal for a variety of use cases, most commonly when you're building some shared component toolkit. As such, Nx comes with a dedicated package `@nrwl/storybook` that adds a series of useful code generators to add storybook to an existing React library, but also to automatically generate stories and more.

Use Storybook to Develop React Components in Nx

[Nx](https://nx.dev) combines a lot of the best tools on the market, integrates them, and provides tooling to provide the best possible developer experience. One such pair is [Cypress](https://www.cypress.io/) and [Storybook](https://storybook.js.org/). Storybook allows developing components in isolation, which is also a great basis for testing components, which is where Cypress shines.
In this lesson, we're exploring some of the `@nrwl/storybook` code generation utilities that allow you to not only configure Storybook for a React application but automatically also set up a Cypress test configuration for it along with some spec files generated from the Storybook stories.

Use Cypress and Storybook to test your React Components in Isolation

Tuples are immutable so there is no way to add or replace items in tuples. There is a way to get around this by adding two tuples into one. 

Understand Immutability with Python Tuples

We are going to go ahead and install globally create-elm-app (because it has the best DX so far) and show how would you typically start any production-ready Elm app, and also add elm-ui as a dependency and show the first elements with elm-ui.

Bootstrap an elm-ui Application with create-elm-app

To showcase the power of elm-ui, in this lesson we will show how incredibly easy it is to center an element both horizontally and vertically on a screen with elm-ui, since this is a typical problem while doing regular CSS.

Center an elm-ui Element Horizontally and Vertically

In this lesson we will start to get to know the row and column functions in elm-ui to help us layout our application. We will choose the mobile first approach in which we will display everything in one column, show how to handle two columns and explain that later when we do responsible we will be able to play a bit more with this. Since the concept of margin does not exist in elm-ui, in this lesson we learn how to solve it by using spacing and we will talk about padding in a future lesson.

Model a Layout with row, column and spacing with elm-ui

We will use the pink box element that we had from previous lessons while implementing our fancy dashboard and we will turn it into our Card "component", the piece of UI that we are going to repeat and that will have its own state later on the course. 

To do this, we'll first manipulate the background color and text color, then change the height and width of the card. We will want to change the title for each of the cards and to do so we will accept a title argument that will be passed down to our text element within the card to be displayed.

Use View Functions to Create Reusable Pieces of UI in Elm

Since we mentioned in a previous lesson that there is no concept of "margin" in elm-ui, we are goint to learn how to se `padding`, `paddingXY` and `paddingEach` to compensate for that, and a little trick defining a value edges that is going to be pretty useful.

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