Learn Flexbox, Prisma and Python

(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.

prisma

Build a full-stack app with Prisma

It's tempting to pick up a framework which has preselected flexbox or grid as the layout tool of choice, but it's important to learn when and why to use each method. This leads to slimmer stylesheets which results in better site performance, and less likelihood for "hacks" to make one method do what the other does inherently better.

Throughout this collection, we will be progressively styling a responsive landing page template while building your knowledge on when to select grid or flexbox for any layout scenario. Spoiler alert: sometimes the best solution is using both!

The specification describes a CSS box model optimized for user interface design. In the flex layout model, the children of a flex container can be laid out in any direction, and can “flex” their sizes, either growing to fill unused space or shrinking to avoid overflowing the parent.

flexbox

Create a Landing Page with CSS Grid and Flexbox

This collection explores one of the most promising and coolest releases of early 2020 - prisma v2. First part of the prisma infrastructure to exit experimental mode - prisma client is an auto-generated and type-safe query builder for Node.js & TypeScript. We will explore and learn it  in this collection!

Get started with Prisma v2. Prisma Client

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

Flexbox is a powerful way to layout HTML so that elements can grow to fit the size of their container.  It can also control how the elements are positioned within a flexed container.  

Once you start thinking in `rows` and `columns`, you can control almost any flexed layout with just a few simple flexbox layout primitives.

Get Started with Flexbox CSS Layouts

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

This playlist covers various aspects of CSS including different rules, selectors, and patterns

CSS Tips and Tricks

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

Flexbox is a wonderful tool built into the CSS specification. Using flexbox doesn't require any special framework or library, just a browser with CSS3 support. It is so awesome, and makes the arranging elements on a page almost fun!

Flexbox 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

After setting `display: flex` on a parent element, you can use `flex-direction` to switch the container to be either a `row` or a `column`.

We also switch the body between `row` and `column`.

Flex into a Row or a Column with flex-direction

We can space items inside of a flexed component in the "on-axis" direction, with `justify-content`.

Justify content can take the following values: `flex-start`, `flex-end`, `center`, `space-between`, `space-around`, or `space-evenly`

Change the Spacing of Flexed Components with justify-content

We changed the axis layout with 'justify-content', and the "off axis" layout is controlled by 'align-items'.  The most common values are `flex-start`, `flex-end`, or `center`.

Change the off-axis Alignment of a Flexed Container with `align-items`

To set the wrap settings of elements inside of a container, we can set the `flex-wrap` css attribute.  The most common values are: `nowrap`, `wrap`, or `wrap-reverse`

Wrap Elements Inside of a Flexed Container with `flex-wrap`

To stretch individual elements to fit the given space, we can set the `flex-grow` or `flex` attributes to any integer.  Other surrounding elements will grow proportionally to their `flex-grow` or `flex` attributes.

Proportionally Grow a Flexed Element by Setting `flex-grow` or `flex` Styles

In this tutorial we will spin the docker container with official postgres image and access the database with `psql` to set up an easy playground for PostgreSQL relational database.


Run postgres database in docker container

In this lesson you write initial boilerplate for prisma (spoiler: it's not bigger than this summary) with typescript infrastructure.

Create new project with prisma

In this lesson you will learn how to connect your prisma to postgres db to be able to introspect it. We will also go trough the models and touch prisma naming convention.

Connect prisma to PostgreSQL database running in docker container

In this lesson you will learn how to generate type safe orm client with `prisma generate` and do first write operation using it.

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