Learn Python, Recompose and Sequelize

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

***Note: Active maintenance for Recompose has been discontinued since the announcement for React hooks. Your existing code using Recompose will still work. Recompose is not compatible with RxJS version 6 and beyond. You can read more about the decision [here.](https://github.com/acdlite/recompose/issues/756#issuecomment-438674573)*** 


Loading data and handling events almost always introduces layers of complexity into React apps. Recompose and RxJS solve your async problems by using Streams to output components. This course walks you through combining the powers of React and RxJS to stream data into JSX with streaming patterns and pure React components.

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

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Build React Components from Streams with RxJS and Recompose

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, you will learn to use many of the most powerful and convenient higher-order components within Recompose by coding several real-world examples. Using recompose looks very different from “vanilla” React; so you will also get comfortable with the look and feel of a “recomposed” component. The best part is no more need for class components! You’ll learn how to use Recompose to both modify components and also create your own higher-order components.

Higher Order Components with Functional Patterns Using Recompose

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

When you declare your Component and Props in JSX, you can pass those props along to your [RxJS](/q/rxjs) stream. This is typically done using `switchMap` or `combineLatest` where you can grab from the props from your `props$` stream and push them into another stream.

Pass a React Prop to a Stream in RxJS

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

Learn how to use the 'compose' function to mix together HOCs, even ones from other libraries like 'connect' from redux.

Compose Multiple Higher Order Components Together using Recompose

Learn how to use the 'withState' and 'withHandlers' higher order components to easily add local state to—and create a reusable local state pattern for—your functional stateless components. No need for classes!


Add Local State to a Functional Stateless Component using Recompose

Learn how to use the 'withReducer' higher order component using the alternative reducer form. If you like using reducers in redux, you’ll be able to reuse that same technique but for local state.

Add Local State with Redux-like Reducers using Recompose

Learn the proper patterns for writing a higher order component from scratch.

Write a Higher Order Component from Scratch

Learn how to use the 'mapProps' higher-order component to modify an existing component’s API (its props). 'mapProps' takes incoming props and changes them however you’d like; for example, filtering the props by a field.

Transform Props by using the Recompose mapProps Higher Order Component

Learn how to use the ‘withProps’ higher order component to pre-fill a prop, unable to be overridden.

Lock Props by using the Recompose withProps Higher Order Component

Learn how to use the 'lifecycle' higher-order component to conveniently use hooks without using a class component.

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