Learn Jest, Neo4j 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 this collection, we build a minimal flashcard application using React, primarily focusing on function components and the hooks API. To make sure we can understand and maintain existing code, there are also a few lessons toward the end that use class components so you can get familiar with the syntax. We cover important React concepts, and sprinkle in libraries that are pretty standard in the ecosystem like @reach/router, Jest, and React testing library. We start with create-react-app so we can get right into it without worrying about configuring tooling. There's [a separate course for that here](https://egghead.io/courses/modern-javascript-tooling-with-react?af=5mx0ie)

jest

Build a React App with the Hooks API

In this course we will learn some of the basics of getting data into and out of Neo4J, as well as touch on some of the libraries and tooling available in the Neo4J community ecosystem, which we will leverage to quickly create and use a full-featured GraphQL API.

We will start by learning how to run and interact with Neo4J locally. Then we will develop a graph model in which to store the data that we pull from SWAPI via REST. We will then create an Apollo GraphQL API driven by a few simple schemas which fit into this model. We will then write some simple code to crawl SWAPI and insert the data into Neo4J through our GraphQL API. Once there is data in the store, we will learn about some of the features in `neo4j-graphql-js` that make connecting and accessing our graph Nodes easier.

Neo4j delivers the lightning-fast read and write performance you need. Neo4j combines native graph storage, scalable architecture optimized for speed, and ACID compliance to ensure predictability of relationship-based queries.

neo4j

Build a Neo4J & GraphQL API

In this course we are going to work through properly setting up Enzyme with Jest to test rendered components. This includes the nitty gritty of making our testing environment work with various versions of React. After we get our environment setup ready, we’ll work through the different ways we can render a component within our test pages. With a rendered component we can now test component methods and properties to assert that they are what is intended. 

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

Test React Components with Enzyme and Jest

End-to-end testing assures you of a few things: that all the integrated pieces of an application properly function, all those pieces work together as expected, and your tests include and simulate real-user scenarios — something unit and integration tests just don't do.

This course introduces you to two great tools for E2E testing: 

* Puppeteer opens and runs applications in a Chromium browser and performs the actions it's given.
* Jest asserts that the integrated pieces work as expected and that add-on libraries like Faker and pixelmatch take our tests to the next level.

Over 9 lessons, we will create common application features and use Puppeteer and Jest to run different types of tests against them. You'll get practice using these tools to render content, mimic user activity, automate tests, and measure the overall performance of your application.

End to End Testing with Google's Puppeteer and Jest

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

Here are all the Egghead.io lessons about testing with Jest! http://facebook.github.io/jest (https://kcd.im/egghead-jest)

Testing JavaScript with Jest

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

Kent walks us through the internals of dom-testing-library and react-testing-library. 

There is a lot of set-up and tear down when testing UI components. These two libraries take away a lot of the work required to test React components well. 

Jest is commonly used in tandem with react-testing-library to assert results of your components. Jest simulates most browser apis with jsdom. react-testing-library helps you get values out of your components the same way your end users will. 

Links from the show:
- [Testing Library](https://testing-library.com)
- [Testing Javascript](https://testingjavascript.com)
- [Unit vs Integration vs E2E](https://kentcdodds.com/blog/unit-vs-integration-vs-e2e-tests)
- [Why you shouldn't ReactDOM.render to the body](https://medium.com/@dan_abramov/two-weird-tricks-that-fix-react-7cf9bbdef375)
- [Effective Snapshot Testing]( https://kentcdodds.com/blog/effective-snapshot-testing/)
- [Dev Tips with Kent](https://youtu.be/ttmvAm6esSc)
- https://kcd.im/news

Understanding how react-testing-library works with Kent C. Dodds

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

To be useful and trustworthy, your CLIs should be extraordinarily reliable. Investing in testing early will reap rewards in future. We show how to set up basic tests with Jest and Oclif's test helpers.

Test Node.js CLIs with Jest and Oclif

Components do more than just render an initial view based on props. Components may also maintain their own internal state and update that state in response to events. Those state changes, will then lead to updates to the rendered output. In this lesson, we'll cover how to test a component and verify that it behaves as expected when we simulate a click event.

Test Component Interactions with Jest and Testing Library

As the core unit of functionality in a React application, our components should be tested. Using Jest and Testing Library, we can render our individual components right in a test context and make assertions about their rendered output and behavior. Tests are a great way to work through API decisions, provide living documentation for your code, and to allow for confidence when refactoring. In this lesson, we'll create an initial test to verify the initial rendered output for a component, given specific props.

Test Component Rendering with Jest and Testing Library

Our unit tests should never rely on code that is outside our control and they certainly shouldn't be calling across the network to a server. A good test needs to be repeatable and fast and relying on outside systems and/or data doesn't help us meet those goals. In this lesson, we'll use mocks to create an environment where we control the response for service calls. This will allow us to be confident in our assertions and ensure our tests aren't flaky.

Mock Modules in a React Component Test

Any time your test interacts with a React component in a way that results in a state update, you need to make sure you wrap that interaction in an `act` function call, otherwise you'll get the "not wrapped in act(...)" warning.

In this lesson you'll learn how you can use `act` with `@testing-library/react-hooks` to avoid potential bugs in your custom React hooks.

This is based on my blog post: [Fix the "not wrapped in act(...)" warning](https://kentcdodds.com/blog/fix-the-not-wrapped-in-act-warning)

Fix the "not wrapped in act(...)" warning when testing Custom Hooks

The dreaded `act(...)` warning is really annoying, but actually really important. I've yet to see it be wrong (even when I thought it must be!). Every time it's pointing out something that is important that I'm missing in my test. In this lesson we'll identify a missing piece to our test and use act to make sure we avoid that in the future.

This is based on my blog post: [Fix the "not wrapped in act(...)" warning](https://kentcdodds.com/blog/fix-the-not-wrapped-in-act-warning)

Fix the "not wrapped in act(...)" warning

There aren't many places you need to manually use `act` if you're using [React Testing Library's async utilities](https://testing-library.com/docs/dom-testing-library/api-async), but if you're using fake timers, you need to manually use `act` to avoid missing bugs in your code.

In this lesson you'll learn how to identify the cause of `act` warnings and how to resolve them.

This is based on my blog post: [Fix the "not wrapped in act(...)" warning](https://kentcdodds.com/blog/fix-the-not-wrapped-in-act-warning)

Fix the "not wrapped in act(...)" warning with Jest fake timers

Use cases for `useImperativeHandle` are few and far between, but if you're testing a component that uses this React hook, you need to wrap any function you expose which results in state updates when called in React's `act` utility. Otherwise you could be missing important things in your tests that could result in bugs shipped to production.

In this lesson we'll learn how to identify what needs to be wrapped in `act(...)` and how to wrap those interactions.

This is based on my blog post: [Fix the "not wrapped in act(...)" warning](https://kentcdodds.com/blog/fix-the-not-wrapped-in-act-warning)

Fix the "not wrapped in act(...)" warning when testing the useImperativeHandle Hook

If you're not careful you can write a test that's worse than having not tests at all. In this lesson we'll learn why that is and how you can avoid it by making sure your test can fail.

This is based on my blog post: [Make Your Test Fail](https://kentcdodds.com/blog/make-your-test-fail)

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