Learn Lodash, Next and Python

Add Conversation Intelligence to your video or voice application without training Machine Learning models. Integrate speech to text and all of the below capabilities, out of the box:

- Speaker Diarization
- Sentiment Analysis
- Contextual Insights
- Conversational Analytics
- Topics of Discussion
- Entity and Intent Recognition
- Pre-built UI and Symbl JS Elements

In this course we will use [Symbl.ai](https://symbl.ai/) to get transcripts for the video we will upload as well as more meaningful insights. 

Additionally, you will pick up NextJS for the main stack and will use Symbl's RESTful APIs for processing a conversation from a video and rendering transcripts to screen. Get your Symbl account [here](https://platform.symbl.ai/) and start building.

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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Create Contextual Video Analysis App with NextJS and Symbl.ai

### Accept payments & sell products powered by Stripe and the best of the JAMStack

There are as many ways to build an e-commerce store on the internet as there are products to sell. One thing is for certain, e-commerce is here to stay and as professional developers we need to understand how to build fully custom stores for our clients using the best modern tools available.

- React: flexible and customizable while following modern best practices
- Next.js: lightening fast with guide rails to help your project perform as consumers expect
- Stripe Checkout: lets you offload reams of complicated business logic to a trusted third party that maintains regulatory compliance, global payments, and a standard UX.

Your store will have well managed local component state using React Hooks and you'll also have clear and cohesive shared (global) state with React Context.

Finally you'll deploy your custom store to Vercel (the platform behind Next.js) as well as how to make your Next.js e-commerce store portable to deploy to other platforms.

Create an eCommerce Store with Next.js and Stripe Checkout

(This collection is still WIP)

Databases are essential for any full-stack applications, but working with them is traditionally very difficult. Prisma is here to change that. It's a powerful database toolkit that helps us think in data itself - not SQL, not classes, just the data we care about in our apps.

In this series, we will learn how to leverage the power of Prisma to automatically generate a type-safe, autocomplete-ready, and efficient query builder, all based on a simple and easy-to-read schema. We will integrate all this good stuff into a Next.js project to complete a simple social network app.

In the end, you will be able to understand how Prisma works, and confidently start using Prisma in your own project.

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

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Build a full-stack app with Prisma

[Next.js](https://nextjs.org/) is a fantastic framework that allows us to build performant server-side rendered React apps with ease.

In this series, we will learn how to utilize the powerful features of Next.js 9, like API routes and Static Site Generation (SSG), to build full-stack React apps. We will use [Auth0](https://auth0.com) to handle user authentication and authorization, and we'll also learn how to integrate the excellent [Chakra UI](https://chakra-ui.com/) component library into our Next.js project to make it a breeze to build beautiful and modularized user interfaces. Finally, we will import and deploy our project from GitHub to [Vercel](https://vercel.com), the deployment platform built by the creators of Next.js.

This collection will still be continuously updated with new and more advanced content. Subscribe to the RSS feed to get notified of the latest lessons!

Introduction to Next.js 9

With the release of `Next.js` v9+, we've been handed the tools to create full-stack react apps with ease. 

This collection will dive into the fundamentals of API & Dynamic routing, handling data from local json and a DB, plus deployment options. 

This collection will be an ongoing collection, where I continue adding more videos over time 😃

Next.js is a minimalistic framework for server-rendered React applications.

NextJS Fundamentals

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.

javascript

Up and Running with AWS Amplify Functions

[Lodash](https://lodash.com/docs/) is a collection of utilities that have become a staple of modern web development. This collection will show you how to re-create its most common methods using simple JavaScript techniques available in any modern web browser. In addition to re-building tools like `_.merge`, `_.get` and `_.debounce` from scratch you will also learn how to replace them completely in some cases with modern syntax such as [optional chaining](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining), [nullish coalescing](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Nullish_coalescing_operator) and [Object.fromEntries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/fromEntries).

This collection should interest the following groups:
- Those who want to better understand common JavaScript patterns
- Engineers who want to reduce their dependence on Lodash (for performance or other reasons)
- Candidates preparing for a technical job interview (where it is common to be asked how to implement some of these methods).

Recreating Popular JavaScript Utility Methods from Lodash

Learn how to use the [AWS Cloud Development Kit (CDK)](https://docs.aws.amazon.com/cdk/latest/guide/getting_started.html#getting_started_install) to create and deploy a [DynamoDB table](https://aws.amazon.com/dynamodb/) with [Next.js](https://nextjs.org).

- 🛠 API Route to handle CRUD actions
- 📦 AWS CDK for Infrastructure as Code
- 🔓 IAM role to restrict permissions
- 🚀 Deploy instantly to [Vercel](https://vercel.com)

You can view the completed code [here](https://github.com/leerob/nextjs-aws-dynamodb). 

Using DynamoDB with Next.js

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

Next.js is a React framework that provides a solutions to problems such as:

- 🌟 bundling & transpiling your React code
- 🌟 code splitting
- 🌟 being able to switch between client side rendering & server side rendering depending on the page (e.g. landing page vs. settings page)
- 🌟 client-side routing

In this collection we're going to learn how to harness the power of Next.js + TypeScript and how to work with Netlify in order to deploy our site for others to enjoy!

Learn how to:
- 🔥 Create a new Next.js project
- 🔥 Add TypeScript support
- 🔥 Create styled components with Emotion
- 🔥 Use both static and dynamic routes
- 🔥 Generate fully typed static pages
- 🔥 Set up Netlify deployments
- 🔥 Add a Netlify Form to our site
- 🔥 and more!

Build a site from scratch with Next.js, TypeScript, Emotion and Netlify

Next.js supports the ability to create APIs based on routes inside of an `pages/api` folder. This opens up a fantastic workflow for incorporating node services into your projects without any configuration. This lesson walks you through the basics of creating your first API route in Next.js

Create an API Route in Next.js

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

Next.JS works with any CSS-in-JS solution, including Styled Components. But to get it working with Next.JS's server-side rendering, you'll need to do some configuration.

In this lesson I go through the steps required to get it working properly, including adding `babel-plugin-styled-components` to your project and overriding the `Document` page to inject your styles into the `<Head>` of the page.

For reference, please see Styled Components' [guide to SSR](https://www.styled-components.com/docs/advanced#server-side-rendering), and the Next.JS [sample repository for Styled Components](https://github.com/zeit/next.js/tree/master/examples/with-styled-components).

Add Styled Components to a Next.JS Project

We begin diving into the Web3 API a little bit deeper during this lesson by taking a test-drive of `eth.sendTransaction` as well as `utils.toWei` method. Before we can dive into using these methods though, we'll first get an understanding of the values that are being used to populate our transaction data prior to send it off to execute on the Ethereum Blockchain.

### External Resources
* [Web3 JS Docs](https://egghead.io/lessons/web3js.readthedocs.io/)
* [Formatic Docs](https://docs.fortmatic.com/)
* [Formatic Developer Dashboard](https://dashboard.fortmatic.com/)

Use the Values from a React Form to Send Ether Transactions to Others with Fortmatic

In this lesson, we will learn how to initialize a Next.js 9 project with TypeScript support from scratch.

Initialize a Next.js 9 project with TypeScript

Next.js treats single-file components in the `/pages/` directory as pages, so we need to work around this abstraction to access and modify the `<head>` element of the page. In this lesson, we will learn how to render the `<head>` element dynamically with a constantly updated title in Next.js 9, and see how this feature can be helpful for loading custom fonts.

Render the <head /> element dynamically in Next.js

Next.js uses a file-system based router, meaning that it treats single-file components in the `/pages/` directory as pages. In this lesson, we will set up multiple pages and use `next/link` to navigate across different routes, and see how to create dynamic routes, and parse URL query strings.

We will be using TypeScript, but for this specific lesson, no changes need to be made if you are using JavaScript.

Understand static and dynamic Page Routing in Next.js

The "API routes" in Next.js are Node.js serverless functions that we can easily incorporate into our app.

In this lesson, we will learn how to write API request handlers, and how to set up dynamic API routes. After this lesson, you'll be able to create simple API endpoints with Next.js ready to be used both in our frontend code within Next.js itself or for other general purposes.

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