Learn Flow, 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.

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Build a full-stack app with Prisma

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.

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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

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 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.

Generate type safe client with prisma

In this lesson you will learn how to perform the simplest `findOne` and `findMany` read operations from postgres database using prisma client.

Perform read operations using prisma client

In this lesson you will learn how to perform the  `update`  operations on postgres table entries using prisma client.

Perform update operations using prisma client

In this lesson you will learn how to perform the `delete` operations on postgres database table entries using prisma client.

Perform delete operations with prisma client

In most of cases your database schema won't stay the same for too long - let's learn how to update the prisma client so it has access to all of the additions made to the postgress

Evolving prisma schema

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

Prisma makes database access easy with an auto-generated and type-safe query builder.

In this lesson, we will learn how to install all the necessary dependencies and set up Prisma for an existing npm project, with an empty PostgreSQL database running in the background. We will create our first Prisma data model in the schema, and use *Prisma Migrate* to map our data model into the database. 

Finally, we'll generate the *Prisma Client* into our `node_modules` directory, so later we can simply import and use the client tailored to our data models as a normal npm package in the project.

At the time of recording this lesson, *Prisma Migrate* is in an experimental state, so the extra flag `--experimental` is needed to perform the `save` and `up` commands.

Install and Set up Prisma for a New Project

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

In this lesson, we will write a code snippet which creates a new entry in the database. We will import and use the Prisma Client that we previously generated by running the command `npx prisma generate` to perform this query. 

Thanks to type-safety, we can now directly catch mismatched data field types while we are coding, drastically reducing the chance of having bugs in the code.

Tip - When in doubt, you can always use the key combination `ctrl-space` to invoke the autocomplete feature of VS Code. This is also a huge benefit made possible by Prisma's type-safety, and is super handy for exploring the Prisma Client API.

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