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

scikit-learn

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

Misunderstanding scope can cause problems in your application. Watch this lesson to learn how Python scope works and the hidden implications it presents. Local scope, nonlocal scope, and global scope variables are demonstrated and explained.

Understand Scope in Python

A module is a function extracted to a file. This allows you to import the function and use it in any other code you may write. You’ll learn how to create modules, import them, and make them stand-alone as you learn what `if __name__ == “__main__”` means in Python.

Reuse Code in Multiple Projects with Python Modules

Why should you have to write all the code? Why can’t you leverage code written by others? I’ve got great news: you can. You’ll learn how to search pip, Python’s package installer, to find and install packages to add new features and functionality to your application.

Manage packages with pip in Python

Object oriented classes work much like classes in other languages. Learn how to create them and use them, learn the difference between class variables and instance variables, creating class methods, and learn how to create classes that inherit from other classes.

Use Python Classes

Getting input from the user via a console command can be done with `input` in Python. Learn how to implement it and validate user input in this lesson.

Read Input in Python from the Console

Exceptions cause your application to crash. Handling them allows you to recover gracefully and keep your application running. Learn how to handle exceptions, find what exceptions you should be handling, and exit gracefully in this lesson. You will also learn how you should *not* handle exceptions and when it is better to let your application crash rather than swallowing an exception. 

Handle Exceptions to prevent crashes in Python

This lesson will introduce arithmetic operations in Python. In addition to basic addition, subtraction, multiplication, and division, you will learn about the differences in division between Python 2 and Python 3.

Perform Math Operations in Python

The tuple is a new data structure to Python, and is an immutable sequence of values separated by commas. Learn how to create them in this lesson.

Create Immutable Values in Python with Tuples

In this lesson you will learn how to install Python. If you use OS X or Linux, you already have Python 2 installed. I will show you how to install Python 3 on OS X, Linux, and Windows.

Install Python

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

Virtual Environments ensure that dependencies from one Python application don’t overwrite the dependencies of another application. In this lesson, you will learn how to create a virtual environment, switch between virtual environments, and manage dependencies within a virtual environment.

Manage Dependencies with Python Virtual Environments

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.

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