Learn Cycle, Python and Xstream

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

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

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream. 

Cycle.js builds on functional and reactive streams and is a JavaScript framework. What does that mean? In the course, André Staltz, the creator of Cycle.js, will explain it to us.

We'll start this course with an empty project and build out our own toy version of Cycle.js to understand exactly what's going on under the hood. When we have a solid base of how Cycle.js works we will move on to use the Cycle.js API and build an app using the Model-View-Intent pattern.

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream.

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Cycle.js Fundamentals

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

We are starting to get a better architecture for these UI apps. But we still have a problem of repeating code for the sliders, since they share so much in common in looks and functionality. This lessons shows how we can create a generic labeled slider as a main() function receiving properties as sources.

Create a Generic Cycle.js Component

Our app is not yet a BMI calculator, because it only has two sliders, but doesn't show the calculated BMI number. In this lesson we will learn how to export any stream from a child component and use it in the parent component, in order to display the BMI calculation on the DOM.

Export Values from Components through Sinks in Cycle.js

This lesson shows what can be learned next as a continuation of this course, and gives a recap on the core concepts: main for pure logic, drivers for side effects, run() to connect main and drivers, sources for read effects, sinks for write effects, and nesting Cycle.js apps to work as components.

Overview of Cycle.js

Let's see next how to use the HTTP driver to generate requests and capture responses. We will build a small app to fetch a single piece of data from a REST server and display that on the DOM.

Fetch Data using the Cycle.js HTTP Driver

Once we have a labeled slider component inside the main function, we should be able to create another labeled slider. This lesson teaches how to accomplish that, and make sure those two instances work independently of each other.

Use Multiple Independent Instances of a Cycle.js Component

We added classNames to pre-processing and post-processing steps when calling the LabeledSlider, in order to instances independent of each other. It would be better if we could hide these steps away. This lesson introduces the isolate() helper function to achieve that.

Isolate Component Instances in Cycle.js

This lesson is the first in an introduction to Cycle.js. We will see how to build from scratch a toy version of Cycle.js. See how to use RxJS to create simple web apps, and how to separate logic from effects.

Separate Logic from Effects in Cycle.js

We need to give structure to our application with logic and effects. This lessons shows how we can organize our code into two parts: main() function for logic, and effects functions for effects.

Organize Code in the Main and Effect Functions

How can we show one string on the DOM, and a completely different string on Console log? This lesson shows how we can make our main function return multiple Observables, each one targeted at a different type of effect.

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