Learn Aphrodite, Loopback 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

In this course the developer will learn how to create a LoopBack API and build it out to an API that can be used as a backend for apps. We will start by creating a basic LoopBack API project and gradually enhance it to a production ready API. The API we create defines a few models to persist the data and has custom functionality, validation and hooks added to those models. 

After our data structure is defined we will learn how to extend the functionality of LoopBack by using mixins and components. We will also lock our API down using ACL’s and create a mechanism to automatically define the admin and user roles and create our admin user.   

The last part of the course will show how how to filter the data stored in the API. We will make the API production-ready by adding unit tests, connecting it to MongoDB and we will show how to deploy the app to now.sh.   

LoopBack is a highly-extensible, open-source Node.js framework.

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Build a Node.js REST API with LoopBack

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

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

A relation in LoopBack is a way to associate the data from multiple models with each other. LoopBack supports various types of relations each with a different use case.

In this lesson we will create a second model called Category and create a relation between the Category and the Product models. To do this we will first add a property to the Product model called `categoryId`. This will allow us to store for each Product the category it belongs to. We then move on to create the actual relationships. 

Our first relationship defines that a `Category hasMany Products` and the second one that a `Product belongsTo a Category`.

After defining these relations we will use the API Explorer to see how we can interact with this related data.

Create a relation between two LoopBack models

In this lesson we will look at the filters that can be used to retrieve and manipulate the data stored in LoopBack. 

We will use [Postman](https://www.getpostman.com/) to interact with the `/Categories` and `/Products` endpoints. 

Using the URL parameters we will apply at the various filters LoopBack has to offer: `include`, `order`, `where`, `fields`, `limit` and `offset`. 

Filter model data in LoopBack

In this lesson we look at extending the functionality of LoopBack models by defining a remote method. 

A remote method is a method on a model exposed over a custom REST endpoint. 

Using the `lb remote-method` command we will create the remote method meta-data in `product.json`. We will verify that this got created and that we see the new REST endpoint got added. Then we will create the actual method in `product.js` and enhance it so that the API will return an error if we want to buy a negative amount of products.

Define a remote method on a LoopBack model

In this lesson we will create a dynamic datasource in LoopBack. If the API is started with the environment variable `MONGODB_URL` it will use this url and the `loopback-connector-mongodb` package to store the data in MongoDB.

In the course we will use a local MongoDB instance. If you don't have MongoDB running locally you can always create a [free MongoDB Atlas database](https://egghead.io/lessons/create-a-free-database-on-mongodb-atlas).


Create a MongoDB DataSource in LoopBack

In this lesson you will learn how to persist the data from the memory connector. As the name suggests, the memory connector stores the data in memory. This means that if you restart the server, the data is gone. In development mode it can be useful to store this data in a file, so it gets persisted between server restarts, and it can be easily inspected.

Persist in-memory data to a file when developing Node.js LoopBack API's

In this lesson we will learn to create a development mode for our server. We do this by installing nodemon as a dev dependency and creating a script tag called 'dev'. 

This script tag will execute the command `nodemon server/server.js --watch common --watch server`.  

We can execute this command by running `npm run dev`. 

When running in development mode the server will be automatically restarted when there are changes detected in the `common` and `server` directories.

Setup a development mode in LoopBack API using nodemon

In this lesson we will learn how to protect our API using ACL’s. 

ACL stands for Access Control List and it's function is to control permissions of resources in the API. It does this by keeping a mapping between an API resource and a principal. An API resource as an API endpoint like a remote method or a whole model. A principal in LoopBack are users or applications that can be grouped in a role.

In order to get the API production ready we will protect a selection of our REST endpoints with ACL’s. We will look at how we can obtain an access token and how we can use that in our requests.

Finally we will write some unit tests to make sure that the ACL does what we expect. We enhance our test setup by exporting `request` with is provided by the [supertest](https://www.npmjs.com/package/supertest) library.

In our tests we use `request` to verify that our endpoints return the correct HTTP status code.  

Configure ACL’s to protect the LoopBack API

In this lesson we will look at how to deploy our LoopBack API project using [now](https://zeit.co/now). 

We will install the now client, create an account and deploy our app.

Using `now secrets` features we will store our database connection url which we get by creating a [free MongoDB Atlas database](https://egghead.io/lessons/create-a-free-database-on-mongodb-atlas).

In order to simplify following deployments we will add a `now` key to our `package.json` where we use the `name`, `alias` and `env` parameters to configure our deployments. We will add some npm scripts so we can deploy our app in the desired way using `npm run now`.

Deploy a LoopBack API project using now.sh

In this lesson we will create a free account on monogdb.com. After the account has been created we will create a group in MongoDB Atlas and in that group we create a free cluster. In order to create the cluster we need to provide a username and password. We create our user called `admin` and have the site generate our password for use. Once the cluster is created we look at where we can find the connection string.

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