Learn Puppeteer, Scikit-learn and Tmux

OpenGraph images are the images you see when you paste a link into a social platform and it "unfurls" into an image, title, and description. This happens on Twitter, Discord, Slack, and many other platforms. This collection goes over everything from designing OpenGraph images in Figma, to implementing them in CodeSandbox, to returning headless browser screenshots from Netlify Functions, and finally using Cloudinary as a write-through cache. 

You will come away from this collection with the ability to ship an API that you can use on any of your sites, and also on-demand, that can generate images for not only OpenGraph unfurls, but also for Instagram, GitHub, and more. Using headless browsers with playwright to generate our images means we get full access to all the responsive power of CSS and all the logical power of JS to handle layout, importing assets like pngs, choosing different fonts, and more.

The image below is generated via the project you'll build and deploy in this course

![An example opengraph image](https://relaxed-payne-d1bfbe.netlify.com/opengraph?title=Building%20an%20OpenGraph%20image%20generation%20API%20with%20Cloudinary%20Netlify%20Functions%20and%20React&tags=figma,netlify%20functions,playwright&author=chrisbiscardi)

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.

react

Building an OpenGraph image generation API with Cloudinary, Netlify Functions, and React

End-to-end testing assures you of a few things: that all the integrated pieces of an application properly function, all those pieces work together as expected, and your tests include and simulate real-user scenarios — something unit and integration tests just don't do.

This course introduces you to two great tools for E2E testing: 

* Puppeteer opens and runs applications in a Chromium browser and performs the actions it's given.
* Jest asserts that the integrated pieces work as expected and that add-on libraries like Faker and pixelmatch take our tests to the next level.

Over 9 lessons, we will create common application features and use Puppeteer and Jest to run different types of tests against them. You'll get practice using these tools to render content, mimic user activity, automate tests, and measure the overall performance of your application.

jest

End to End Testing with Google's Puppeteer and Jest

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

Learn to manage your terminal sessions and work more effectively from the command line using tmux. If you use the command line at all, tmux can make your life easier.

tmux bills itself as a terminal multiplexer. It allows you to wrangle multiple terminal sessions from one window. Instead of keeping track of many terminal windows yourself, you can use tmux to create, organize, and navigate between them. Even more importantly, tmux lets you detach from and re-attach sessions, so that you can leave your terminal sessions running in the background and resume them later. This is especially useful if you're working on a remote server: you can set up a persistent session that will continue running when you close your laptop. You can even share a tmux session to facilitate pair programming.

It lets you switch easily between several programs in one terminal, detach them (they keep running in the background) and reattach them to a different terminal.

tmux

Wrangle your terminal with tmux

In this lesson, we'll learn how to detach from a running tmux session and leave it running in the background. Then, we can re-attach to it later.

We'll cover:
- How to exit a session by killing all active panes
- Detaching explicitly with C-b d
- Detaching a specific session with C-b D
- Viewing all running tmux sessions using tmux ls
- Reattaching with tmux attach -t

Reuse terminal workspaces using tmux sessions

In tmux, a window is a collection of panes. Creating multiple windows is a great way to organize your workspace. In this lesson, we'll cover:

- How to create a new window within a tmux session, using `C-b c`
- Navigating between windows with `C-b n` and `C-b p`

Create collections of panes using tmux windows

In this lesson, we'll look at how to manage your history between tmux sessions, and ensure that your setup preserves your bash history between multiple windows. By adding a special PROMPT_COMMAND to your .bashrc, you can update and reload your history after every command, regardless of if you're in a different session, window, or tab.


Handle history in tmux sessions

Sometimes you'll want to zoom in on a specific pane within a tmux window. We'll learn to use C-b z to temporarily zoom in on one pane, as well as how to resize your panes with C-b -L, -R, -U, and -D.


Zoom and resize to view a particular pane within tmux

Learn to organize your workspace using tmux. We'll create a new tmux session and learn how to create and navigate panes within a tmux window.

We'll cover:

- How to create new panes via vertical splits with C-b %
- How to create new panes via horizontal splits with C-b "
- Navigating between panes with C-b and arrow keys
- Closing panes with exit or Ctrl-d
- Viewing available commands with C-b ?

Organize your terminal using tmux panes

It's a lot easier to manage your tmux session when they have sensible names. We'll cover:

- How to create a new, named tmux session with tmux new -s
- Rename an existing tmux session using tmux rename-session -t
- Renaming sessions with C-b $
- Listing sessions with C-b s

Manage terminal workspaces using session naming

One non-obvious, but extremely useful, feature in tmux is copy-pasting text between panes. This also allows you to scroll back in a tmux sessoin. We'll cover how to use copy and paste in tmux using C-b [ and C-b ], for both emacs- and vim-style keybindings.

Copy and paste text from a tmux session

You can modify tmux's behavior with your tmux configuration file, .tmux.conf. You can use your tmux config to change color schemes, set custom keybindings, set defaults, modify the status bar, and more. In this lesson, we'll change some of the default color options as well as modify the prefix key.


Customize tmux with tmux.conf

By using ssh, you can share a tmux session, making pair programming much easier. We'll learn how to share a tmux session with another user. 


Share a tmux session for pair programming with ssh

We'll learn how to use mouse mode in tmux, including enable mouse control for resizing, scrolling and selecting panes. We'll also set keybindings in our tmux configuration to control mouse mode. 

Enable mouse mode in tmux

Do you have a standard workflow that involves setting up a specific tmux layout, or running certain commands? By writing tmux scripts, you can automate your tedious daily setup, while simultaneously documenting it for yourself. We'll cover the tmux command equivalents of common key bindings and write some simple, useful scripts.


Automate your workflow using tmux scripts

Linear regression is a linear model that is used for regression problems, or problems where the goal is to predict a value on a continuous spectrum (as opposed to a discrete category).

We’ll use linear regression to estimate continuous values. In this case, we’ll predict house prices in Boston. We'll also look at how to visualize our results with matplotlib, and how to evaluate our models with different metrics for regression problems.

Use Linear Regression To Estimate Continuous Values with Python and Scikit-learn

Despite its often confusing name, logistic regression is a **linear** model that is used for **classification**, or estimating discrete values.

We'll use an inbuilt scikit-learn dataset of iris data to classify irises into three categories. We'll also look at metrics and tools to evaluate our classification models, including the accuracy score, classification report, and confusion matrix.

Predict Categories in Python using Scikit-learn's Logistic Regression module

We’ll continue with the iris dataset to implement support vector machines, which can be used to find more complex boundaries for classification or regression problems. 

More about Support Vector Machines can be found at [scikit-learn](http://scikit-learn.org/stable/modules/svm.html).

There are several types of kernel function that can be used with SVMs. Scikit-learn supports these kernels:

-linear

-polynomial ('poly')

-rbf (radial basis function) 

-sigmoid 

Custom kernels are also supported. `Rbf` is the default kernel type.

A good overview of kernels can be found [here](https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html), or at the [scikit-learn page](http://scikit-learn.org/stable/modules/svm.html#svm-kernels).

Use Support Vector Machines To Find Complex Decision Boundaries with Scikit-learn

We’ll continue with the iris dataset to implement k-nearest neighbors (`KNN`), which makes predictions about data based on similarity to other data instances. We'll visualize how the `KNN` algorithm works by making its predictions based on its neighbors' labels. We'll also examine the confusion matrix a bit further.

`KNN` can be used for both classification and regression problems. 

`KNN` is good for low dimensional data (data without too many input variables). It is not good for unbalanced data sets, and it can be computationally expensive.

Use K-nearest Neighbors To Find Similar Datapoints with Python and Scikit-learn

We’ll learn about decision trees, also known as CART (classification and regression trees), and use them to explore a dataset of breast cancer tumors. 

We'll also see how to visualize a decision tree using `graphviz`.

Implement Decision Trees in Python with Scikit-learn

We’ll return to the iris dataset to see how to use k-means clustering, an unsupervised learning algorithm, to create  categories for data that doesn't have labels. We'll also visualize these clusters using matplotlib.

More on K-means can be found at [Scikit-learn](http://scikit-learn.org/stable/modules/clustering.html#k-means).