Learn Nativescript, Parti-corgi and Python

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parti-corgi

How to Livestream Code and Design on Twitch

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.

python

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

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

NativeScript has the ability to write Objective C, Swift and Java apis directly from JavaScript and TypeScript. 

In this course, we will learn how to code directly against these native api’s with NativeScript for Angular. Many libraries are written and published in these languages. With NativeScript you are no longer limited to just JavaScript based libs, but have direct access to these api’s right from JavaScript/TypeScript. Let’s look at how to include these native libraries internally to your project as well as build public plugins to share with the community. 

We will cover some considerations with Objective C and Swift based libraries as well as Java/Android libraries. You can think of CocoaPod, Gradle, and Android Arsenal like npm for native libs. iOS libraries can be found via CocoaPods, Android libraries can be found via Gradle or Android Arsenal. 

Let’s also cover how to write custom view components based on native libs since there are considerations for both iOS and Android. Lastly we will learn how to best handle cases where a plugin/library only supports 1 platform.

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

nativescript

Use Objective C, Swift and Java api’s in NativeScript for Angular iOS and Android apps 

In this course, we will learn how to use the core layouts for NativeScript apps. We will start with the basic principles of NativeScript layouts using XML. This course will use the app templates provided from the NativeScript framework and installed using [the NativeScript CLI](https://egghead.io/lessons/angular-1-x-set-up-a-native-app-project-with-the-nativescript-cli).

We will cover the following layouts: `StackLayout`, `WrapLayout`, `GridLayout`, `DockLayout`, `AbsoluteLayout` and `FlexboxLayout`. Understanding the concepts and usage of UI layouts in NativeScript is key for a great app.

Build Basic NativeScript App Templates

In this course, we will learn how to work with NativeScript for Angular. We will start with the basic fundamentals of getting setup to run our app in the iOS Simulator in addition to an Android emulator using GenyMotion (https://www.genymotion.com/). Understand how to build your native mobile views with NativeScript XML and how to style them with CSS. Using the NativeScript for Angular starter template provides a nice setup out of the box including the core theme which provides handy CSS classes to work with speeding up view building. For page navigation, we will learn how to use the Angular Router to configure a flexible routing setup for our app. The ListView component is a powerful and highly performant native control for handling your lists powered by the UITableView on iOS and native ListView widget on Android. We’ll highlight how you can use RxJS Observables with our native views. Let's also take a look at integrating npm plugins to enrich the UI component library. Lastly, a round up of styling how-to's including integrating SASS for custom app theme creation as well as creating a live app theme switcher.

This Angular course assumes a solid understanding of the fundamentals.
We have Intermediary Angular courses including topics on [Dependency Injection](https://egghead.io/courses/angular-dependency-injection-di-explained), [Components, Directives](https://egghead.io/courses/building-angular-components), [Forms](https://egghead.io/courses/intro-to-angular-forms), [Router](https://egghead.io/courses/intro-to-angular-router), and [Animating Web Apps](https://egghead.io/courses/animate-angular-web-applications)

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

Create Native Mobile Apps with NativeScript for Angular

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.

javascript

Up and Running with AWS Amplify Functions

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

We will learn how to add an Android library to our project and translate it’s Java api to NativeScript. For this lesson, we will look at [Filestack’s Android SDK](https://github.com/filestack/filepicker-android) built with Gradle. Specifically, we will cover how to immediately start working with the library even when a [NativeScript](/q/nativescript) plugin does not exist for it.

Use an Android library in NativeScript for Angular using Java apis

Learn how to use the `nsRouterLinkActive` directive with options to control styling when a route is active in [NativeScript](/q/nativescript).

Add styles to active routes using nsRouterLinkActive

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

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

Many streamers use colorful banners on their stream pages that reflect their branding. Here's how you can create and use them.

* [Panel Maker](https://nerdordie.com/resources/free-resources/customizable-twitch-panels/)

Creating Panel Banners on Twitch

A short look at what I do before I change from my "Starting Soon" scene to my "live scene" and a brief overview of what to except from the collection

Introduction to Livestreaming

We cover some of the twitch interface and open a twitch account

Starting a Twitch account to livestream

We introduce OBS and compare it to SLOBS, download and set up our first scene including desktop capture and audio tips.

Introduction to OBS and creating your first scene

We cover how to get your stream key, how to use that stream key to test bandwidth and CPU usage settings, and go live on Twitch for the first time.

Going live on Twitch for the first time and testing bandwidth

While there are many useful tools and toys to pull together for a livestream, the truth is you don't need any of them to start.

What gear do I need to livestream?

It's likely that you will have many streams where no one is there, or people are there and they aren't telling you they are. Does it matter?

What if nobody comes to watch my livestream?

https://www.elgato.com/en/gaming/stream-deck

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