Learn Angular-cli, Pm2 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

PM2 is a production process manager for Node.js applications with a built-in load balancer. It allows you to keep applications alive forever, to reload them without downtime and to facilitate common system admin tasks.

This video shows how to enable features in your pm2 config file that allow you to prevent runaway apps from bringing your server down. Setting max memory used, number of running processes allowed and maximum restart attempts are all covered.

Controlling runaway apps using pm2

angular-cli

Generating components with the Angular CLI allows for scaffolding applications quickly. Learn what will be generated for you and how to generate quickly with shortcuts in the desired directory.

Generate components with the Angular CLI

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

Getting started with angular development can be difficult, with a lot of moving parts. Allow the Angular CLI to handle those complexities so you can focus on creating great applications.

In this lesson you will learn how to install the Angular CLI from npm, create a new application, serve the new application in the browser and edit it with live-reload.

Install the Angular CLI to Create and Serve an Angular Application

In this video, you will learn how to pass environment variables to your node.js app using the pm2 config file. This is useful for setting options inside your app such as production or development.

Passing environment variables to node.js using pm2

In this video, learn how to automatically restart your node.js app using pm2 when the code for your app changes. You will also learn how to exclude files and directories from restarting your app (such as static assets, css, images, etc.).

Restarting your node.js app on code change using pm2

In this lesson we will our `UiModule`, that has a `LayoutComponent`, `HeaderComponent` and `FooterComponent`. 

We generate `UiModule` by running the following command:
```
ng generate module ui --module app
```

This generates the new module and imports it in our `AppModule`.

We'll create the `LayoutComponent` using:
```
ng generate component ui/containers/layout
ng generate component ui/containers/header
ng generate component ui/containers/footer
``` 

We update our `AppRoutingModule` and add this component to our first route to make it visible. 

The `HeaderComponent` and `FooterComponent` are referenced in the LayoutComponent and we implement the actual templates!

Create Application Layout with Components in an Angular CLI Project

In this lesson we will add the `ProductModule`, where our actual application code will live.

```
ng g m product --routing
``` 

We will add 2 routes to `AppRoutingModule`,  the first is a redirect route that redirects any unhandled paths to `products`. The second route is the actual `products` route, that 'lazy-loads' the `ProductModule`.

We use the `ng` command to create the `ProductListComponent` and `ProductDetailComponent`, which are so called 'containers components'.

Container components are responsible for fetching data, handling routes and passing data into the 'presentational components' 

We will add 2 routes to `ProductRoutingModule`,  one to each of our new Container Components.

Lastly we will use  `ActivatedRoute` in `ProductDetailComponent` to fetch the `:id` from the route.

Create Container Components for Retrieving Data in an Angular CLI Project

In this lesson we will create the presentational components for the product list and detail pages. These components define how the data is displayed and are being referenced from the Container components.

The new components are `ProductsComponent` and `ProductComponent`.

After using `ng generate` to generate these components, we will update the `ProductListComponent` to invoke `ProductsComponent` and pass in our local `products` array.

In `ProductsComponent` we loop over each of the items we get from the `@Input() products` and pass each one into our `ProductComponent`.

The `ProductComponent` has 2 `Inputs()`, one for the `Product` and a `boolean` that toggles showing the product details. This is useful because don't want to show product details in the list view.

After the `ProductComponent` is implemented the last step is to reference it from the `ProductDetails` container component. 

Create Presentational Components for Displaying Data in an Angular CLI Project

In this lesson we will create the `ProductsService` that is responsible for the interaction with our API. 

We will inject the `HttpClient` that we get from `@angular/common/http`. To make this working we need to import `HttpClientModule` in our `AppModule`.

We define a `getProducts` and `getProduct` method so we can get a list of products and an single product which we will use in respectively the `ProductList` and `ProductDetail` components. 

In both components we will use the `ngOnInit` lifecycle hook to invoke the methods on the service, and retrieve the data by subscribing to it. 

[Link to product api used in lessons](https://github.com/beeman/egghead-products-api)

Create an Angular Service to Retrieve Data from an API 

In our 'container components' we use the `ngOnInit` lifecycle hook to subscribe to our service. While this works, there is a downside: the components get loaded before the data is available, which results in that we show empty components. This is particularly visible on slow connections.

To fix this we create a `ProductsResolver`, and implement the `resolve` method that fetches the data from the service. In `ProductRoutingModule` we add the `resolve` property to our route, and add make the `ProductsResolver` resolve and store the data in the `products` property.

Inside our `ngOnInit` in `ProductListComponent` we can now subscribe to the route data, which makes the usage of the service in this component obsolete.

We do the same for the `ProductDetailComponent`. After this there are no longer empty components, the router will not navigate after the data is retrieved.

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