Learn Elixir, Loopback and Python

These videos include the introductory steps to writing your first Elixir function and beginning to understand some key concepts in Elixir that make it unique. 

Some of those concepts include:
- the Mix build tool
- project structure
- collections of functions written in modules
- function *arity*
- pattern matching and common uses for it
- the pipe operator

Elixir is a dynamic, functional language designed for building scalable and maintainable applications.

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

elixir

Getting Started with Elixir

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

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.

loopback

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.

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

Bootstrap a new Elixir project using Mix (a build tool included when you install Elixir), with its built in project generation.

Additional resources:
- [Official introduction to Mix](https://elixir-lang.org/getting-started/mix-otp/introduction-to-mix.html#our-first-project)

Generate a new Elixir project using Mix

Elixir code is organized into groups of functions called modules. You can create your own modules and built on top of predefined modules that are a part of Elixir by default.

Additional resources: 
- [Getting started with Modules and function from Elixir docs](https://elixir-lang.org/getting-started/modules-and-functions.html)

Write a New Function Inside of an Elixir Module

The syntax to pass arguments into functions is akin to many other languages. Elixir functions are uniquely recognized when running based off of the number of arguments passed in (a term called *arity*). This means you can define functions with the same name, so long as a different number of arguments is passed in.

Arity is a key concept to be aware of when reading documentation and writing your own code.

Additional resources:
- [Information about arity](https://elixir-lang.org/getting-started/modules-and-functions.html#function-capturing)
- [Default arguments in functions](https://elixir-lang.org/getting-started/modules-and-functions.html#default-arguments)

Pass arguments into Elixir functions and understand arity

Elixir uses `=` as an operator, but rather than assigning values to variables with it, it *matches* values on the right hand side of the `=` with the left side. This can be used in simple values, more complex data structures, and powerful condition checking.

Put into simpler terms, pattern matching is Elixir's way of assigning variables.

Additional resources:
- [Elixir docs on pattern matching](https://elixir-lang.org/getting-started/pattern-matching.html)

Pattern match values in Elixir

Some common uses for pattern matching in Elixir include checking conditions and passing parameters into functions. It's useful for pulling out only specific variables from collections of data.

Additional resources:
- [Elixir docs on pattern matching](https://elixir-lang.org/getting-started/pattern-matching.html)

Pattern match in conditions and function parameters in Elixir

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.

Topics

Instructors

Content Type