Learn Jasmine, Karma 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.

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

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

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

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

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

In this lesson Trevor looks at testing AngularJS controllers with scope inheritance with Jasmine and test spies.

Testing Controllers With Dependencies

webpack is a tool to build JavaScript modules in your application. To start using webpack from its cli or api, follow the Installation instructions. webpack simplifies your workflow by quickly constructing a dependency graph of your application and bundling them in the right order. webpack can be configured to customise optimisations to your code, to split vendor/css/js code for production, run a development server that hot-reloads your code without page refresh and many such cool features.

webpack

There are several steps involved with setting up [`Karma`](http://karma-runner.github.io/) to work on your webpack project. In this first step we'll initialize our karma configuration and setup our `package.json` `scripts`.

Initialize a Webpack Project with Karma for Testing

Chai is a BDD / TDD assertion library for node and the browser that can be delightfully paired with any javascript testing framework.

chai

[Chai](https://npmjs.com/package/chai) assertions work great with the [Mocha](https://npmjs.com/package/mocha) testing framework. In this lesson we'll walk through out to install Chai and [karma-chai](https://npmjs.com/package/karma-chai) so you can use the `expect` assertions in your tests.

Use Chai assertions for tests in a Karma project

When writing tests run by Karma for an application that’s bundled with webpack, it’s easiest to integrate webpack and Karma directly together. In this lesson we’ll see how to utilize the [`karma-webpack`](https://npmjs.com/package/karma-webpack) plugin and reuse our existing webpack configuration to preprocess our test files with webpack.

Use Karma for Unit Testing with Webpack

Custom Jasmine Matchers are a great way to keep your AngularJS Unit Tests clean and tidy. We want to make expectations **readable** and express the intent of the test. Custom matchers help to accomplish this.

Custom Jasmine 1.3 Matchers

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

An introduction on unit testing a ReactJS component using Webpack and Jasmine. Learn how to use Webpack to package and import dependencies plus take advantage of the babel-loader to write a simple Jasmine spec using the latest ES6 syntax.

Unit testing a React component using Jasmine and Webpack

Lightweight yet powerful IDE, perfectly equipped for complex client-side development and server-side development with Node.js.

webstorm

A quick tutorial covering installing Karma and running tests from the command line and Webstorm.

Introduction to Karma

AngularJS provides helpers for convenient testing of your application. In this lesson you will learn how to test a basic controller with Jasmine and Angular.

Testing a Controller

In this lesson John shows some tricks for speeding up your test suite. He digs into some handy features of WebStorm, including the *ddescriber* plugin, as well as some alternative methods for Jasmine for selectively running your tests.

Speed Testing with WebStorm and Jasmine

One of the beautiful things about dependency injection in your AngularJS apps is the ability to replace dependencies in your unit tests. In this lesson Trevor looks at how to configure Jasmine spies as injectable replacements for dependencies for testing.

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