Learn Asp-net, Polymer and Python

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

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

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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

Polymer is a framework created at Google for building reusable web components.

We use HTML elements every day such as div, a, button, form, and table. All HTML elements provide some sort of API of attributes, properties, methods and events that we use to manipulate them. Generally, web elements are styled in a particular way by default, and also allow us to customize their styling with CSS.

Polymer allows you to build fully custom web elements of your own for more complex components composed of the building blocks you are used to with the standard set of HTML elements.

This course is an introduction to Polymer, and should get you started building your own customizable web elements that you can use on their own, or even with your favorite framework like Angular or React!

The Polymer library provides a set of features for creating custom elements. These features are designed to make it easier and faster to make custom elements that work like standard DOM elements.

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Creating Custom Web Elements with Polymer 2

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

ASP.NET is an open source web framework for building modern web apps and services with .NET. ASP.NET creates websites based on HTML5, CSS, and JavaScript that are simple, fast, and can scale to millions of users.

asp-net

ASP.NET Core SignalR can push events from the server into your Client Application. In this video we will build a client side Todo app with Angular which consumes events from a ASP.NET Core SignalR backend to see how we can consume and synchronize data between our client side Angular Applications.

Consume ASP.NET Core SignalR values in an Angular Application

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

Providing real time information to your Javascript apps with [ASP.NET Core SignalR](https://www.npmjs.com/package/@microsoft/signalr). With ASP.NET Core and SignalR you can add real time functionality with ease and keep your application up to date without letting the clients reloading their page manually.

Expose Values for Real-time Consumption with SignalR in ASP.NET Core

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

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

In this lesson we'll look at creating event listeners in our custom element using the listeners object as well as annotated event listeners.

Create Event Listeners in Polymer Elements

Custom element property changes can be observed by specifying an observer method which will be executed whenever a change occurs and receive both the new value and old value.

Observe Property Changes on a Custom Polymer Element

In this lesson we'll look at the lifecycle callbacks (Created, Ready, Attached, Detached, and attributeChanged) available to us in Polymer and how to use each one.

Use Lifecycle Callbacks On Custom Polymer Elements

Polymer provides us a variety of data-binding configurations.  In this lesson we'll look at configuring Two-way, One-way-down and One-way-up data binding.

Data Bind Two Polymer Elements

We can utilize the "fire" method from within our custom element to execute a custom event.  In this lesson we'll create a custom event and pass useful data along with the event to the consumer of our element.

Execute Custom Events in Polymer

In this lesson we'll use the "dom-repeat" helper method in our custom element to iterate over a collection and use the built in model instantiation to update items in our collection.

Iterate over a Collection with Polymer's dom-repeate

In this lesson we'll install Polymer using bower and explore creating our first custom element. We will use the dom-module to create our first Element and add an event listener to watch for the `keyup` event.

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