Learn Git, Neo4j and Python

This workshop will start by breaking down the four states in which a file can exist, and we'll build up our mental model of git from there. Then, we'll look at how to move files between those states in different ways.

Once we've developed a new model for how git operates, we'll be able to purposefully get ourselves into some sticky situations, and then gracefully recover from them.

If you can handle git add/commit/push, but not much more - then this workshop is perfect for you.

You can finally get some proper git training, and will never have to worry about losing data or messing up the repo again!

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

Fix Common Git Mistakes

Knowing your everyday toolset increases your productivity as a developer. And Git is one of those tools you use on a daily basis. Mastering Git is tough; it has hundreds of different commands, often even inconsistent at how you pass in flags and configuration options. On the other side, there are a lot of good Git courses out there already, explaining all of these commands in detail. 

With this course, I aim at a different goal. You will walk through a series of scenarios which you’ll most commonly encounter in your daily work life as a developer. I’m talking:
 - updating your feature branch with the latest changes from master
 - polishing your git history to make it ready for being peer-reviewed
 - moving a set of commits to another branch
 - undoing accidental commits

The goal is not to cover everything, but those tasks that will greatly improve your daily productivity with Git.


Productive Git for Developers

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

In this course we will learn some of the basics of getting data into and out of Neo4J, as well as touch on some of the libraries and tooling available in the Neo4J community ecosystem, which we will leverage to quickly create and use a full-featured GraphQL API.

We will start by learning how to run and interact with Neo4J locally. Then we will develop a graph model in which to store the data that we pull from SWAPI via REST. We will then create an Apollo GraphQL API driven by a few simple schemas which fit into this model. We will then write some simple code to crawl SWAPI and insert the data into Neo4J through our GraphQL API. Once there is data in the store, we will learn about some of the features in `neo4j-graphql-js` that make connecting and accessing our graph Nodes easier.

Neo4j delivers the lightning-fast read and write performance you need. Neo4j combines native graph storage, scalable architecture optimized for speed, and ACID compliance to ensure predictability of relationship-based queries.

neo4j

Build a Neo4J & GraphQL API

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

As modern web developers, we are likely to use git on a daily basis. It's a wonderful distributed version control system, and with only a few commands in our toolbelt it is quite powerful and convenient.

In this course, you will learn the basics of using git, as well as some more advanced commands that you should find useful!

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/practical-git-for-everyday-professional-use-notes).

Practical Git for Everyday Professional Use

“Feel free to submit a PR!” - words often found in GitHub issues, but met with confusion and fear by many. Getting started with contributing open source is not always straightforward and can be tricky. With this series, you’ll be equipped with the the tools, knowledge, and understanding you need to be productive and contribute to the wonderful world of open source projects. Much of this series speaks about GitHub, but most of the concepts are generally applicable to contributing to any open source project, regardless of where it’s hosted.

So enjoy the course and start contributing to the projects you use and love today!

How to Contribute to an Open Source Project on GitHub

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

A Unix command used to search files for the occurrence of a string of characters that matches a specified pattern.

grep

When you have a bunch of text to sift through, standard Unix command line utilities like `grep`, `sed`, `awk`, `cut`, `sort`, and `uniq` can help extract the information you want. For example, with a git log for a project, you might want to group commits by day in order to see which days you're most productive, or find out how many commits you made last year. In this lesson, you'll learn how to combine these commands with the pipe operator to extract this data from git logs.

Analyze Git Logs with the Command Line

`git reflog` helps developers recover from their mistakes. The reflog keeps track of any changes or commits to each branch since cloning a repository. It's frequently used to keep track of changes made locally that are sometimes wiped out when pulling in remote changes.

This lesson demonstrates how to use git reflog to restore git commits and branches that have been lost. We will clone a repo, make some changes with commits and branches, remove all of those changes, and then use data from the reflog to restore them.

Restore Commits with git reflog

Git rebase is a super powerful tool, but one many devs shy away from for fear of messing up history. But when you know how to use it safely, you can make your pull requests much easier to understand and reason about.

For anyone interested in the gn log alias I use in this video, it's this:

git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit

Rewrite Git History with Interactive Rebase

This lesson demonstrates how to use the git rebase command to reword a commit message deep in your git history.

Reword a Commit with Git Rebase

In this video you'll learn Git-based deployment for web applications for both front-end and back-end resources using AWS Amplify and the Amplify Console.

Hosting & Continuous Deployment for Front End and Serverless Back Ends using AWS Amplify

Whenever I need to make a change to a file that includes a part I don't grasp I try to gather as much informations as possible. One tool that turned out to be very helpful to build up context was seeing who wrote the code & when.

In this lesson I demonstrate you how this can easily be achieved using the VS Code GitLens extension and how to set it up in a way that it's not constantly bothering you.

Get Git Blame insights directly in VS Code using GitLens

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

If you make a mistake in a commit message but HAVEN'T pushed it yet, you can change that commit message with --amend:

`git commit --amend -m "New message"`

This won't change any of the files in your commit - but will rewrite the commit with the new message.

Change a Commit Message that Hasn't Been Pushed Yet

To add more files to the most recent commit, we can add them to the staging area with:

`git add -A`

and then rewrite the most recent commit to include them with:

`git commit --amend -m "My new message"`

Caution: only do this BEFORE you've pushed the commits - otherwise you will be re-writing history for people who may have already pulled down the old commits.

Add More Files and Changes to a Commit Before Pushing

If you've added files to the staging area (the Index) accidentally - you can remove them using `git reset`.

We'll first add a file to staging, but then back it out with:

`git reset HEAD filename`

in order to pull it back to the working directory, so it doesn't end up in a commit when we don't want it to.

Remove Files from Staging Before Committing

If we've already committed changes that we _don't_ want to push, we can also remove those with `git reset`, but we're going to reset back to a specific commit, either with:

`git reset HEAD~1`

or

`git reset [HASH]`

and that will effectively "undo" the commits before that hash. 

Then we can add and commit only the changes that we want.

Topics

Instructors

Content Type