Learn Safari, Scikit-learn and Xstream

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

screen-reader

Learn the basics of navigating through a web page using VoiceOver on MacOS in Safari.

Navigate a Webpage with VoiceOver in Safari

The Chrome Developer Tools (DevTools for short), are a set of web authoring and debugging tools built into Google Chrome.

chrome-devtools

Using our accessibility auditing toolkit we can find issues concerning landmark regions in our web application.  The react-axe library will report findings in the JavaScript console of the browser.  We could also run the axe browser extension to see the same results.  The tota11y browser extension will annotate any landmark regions on the page and therefore if none are annotated it may indicate that they aren't any (_NOTE: tota11y will only annotate landmark regions using aria roles and not those using HTML5 elements_).  Finally using the VoiceOver Rotor in Safari will show us whatever landmark regions have been defined in the Landmarks menu. 

Test for Landmark Region Accessibility Issues in React

We can find issues with heading levels by utilizing our accessibility auditing tools.  The library react-axe, as well as the browser extension axe, will report any findings concerning heading levels.  The browser extension tota11y will annotate all heading levels on a page and show any in red that have issues.  Finally, we can check the Rotor in VoiceOver in Safari.  The Rotor should have a Headings menu and will list in order all of them that are found on the page.  If headings are missing or inappropriate this will help us find them as well.

Test for Heading Level Accessibility Issues

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

Labeling inputs, elements, and widgets add context and clarity for assistive technology such as screen readers. Some elements do not already have some sort of labeling text being displayed visually, such as a `<button>` without descriptive text or an `<icon>` or part of a more complex widget.  Others may have some form of text label but it is not descriptive enough for those who cannot see it.  The addition of an `aria-label` attribute to the element will provide it with an accessible label so that when that element is focused by a screen reader, for instance, a label is read that better describes the element and what a user can expect from it.

Add Accessible Labels to Elements Whose Labels are Not Clear Enough

Labeling inputs, elements, and widgets add context and clarity for assistive technology such as screen readers. Some elements do not already have some sort of labeling text being displayed visually, such as a `<button>` without descriptive text or an `<icon>` or part of a more complex widget.  Others may have some form of text label but it does not provide enough context to be useful enough for those that cannot see it.  The addition of an `aria-label` attribute to the element will provide it with an accessible label so that when that element is focused by a screen reader, for instance, a label is read provides better context for the element and what a user can expect from it.

Add Accessible Labels to Provide Elements with More Context

Labeling inputs, elements, and widgets add context and clarity for assistive technology such as screen readers.  When there are already one or more other elements that themselves can be used to provide labeling text to another element an `aria-labelledby` attribute can be added to that element.  

Some examples of appropriate use cases for using an `aria-labelledby` are:
* Combine multiple labels together
* Associate headings with regions
* Label for a widget

Add an Accessible Label to an Element from the Text of Other Elements

Labeling inputs, elements, and widgets add context and clarity for assistive technology such as screen readers. Beyond adding accessible labels to elements and widgets we can also provide additional descriptions.  Similar to how an `aria-labelledby` attribute works, an `aria-describedby` attribute can link the text from another element or elements to be used as a description for the given element.  

Some example use cases for using an `aria-describedby` are:
* providing instructions
* providing important usage details

Add Accessible Descriptions to Elements

When dynamic changes occur to content on a page they are usually visually apparent to users who can see the page but may not be made apparent to users of assistive technologies. ARIA live regions provide a way to make those dynamic content changes able to be announced by assistive technologies.

## ARIA attributes
* `aria-live`: the priority with which updates are read
* `aria-atomic`: whether the screen reader should always present the live region as a whole, even if only part of the region changes
* `aria-relevant` - takes a list of what type of changes should be announced

## Resources
* https://developer.mozilla.org/en-US/docs/Web/Accessibility/ARIA/ARIA_Live_Regions
* https://www.w3.org/WAI/PF/aria-1.1/states_and_properties#aria-live
* https://www.w3.org/WAI/PF/aria-1.1/states_and_properties#aria-atomic
* https://www.w3.org/WAI/PF/aria-1.1/states_and_properties#aria-relevant
* http://html.cita.illinois.edu/nav/form/aria/index.php?example=3

Define a Live Region to Ensure Dynamic Changes are Announced by Assistive Technologies

Did you know every Mac comes with a fantastic screen reader built-in? Learn the basics for operating Voiceover with Safari, including common key commands, the web rotor, and the help menu. We'll also set up keyboard tabbing in Safari and OSX's System Preferences, two requirements for accessibility testing that occasionally trip up users. In a future lesson, we'll compare Voiceover to Windows screen readers, since they work quite differently.

#### Resources
- Apple Voiceover commands and gestures: https://www.apple.com/voiceover/info/guide/_1131.html
- WebAIM Voiceover Tutorial: http://webaim.org/articles/voiceover/

Using the Voiceover screen reader to test for accessibility

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream.

cycle

This lesson is the first in an introduction to Cycle.js. We will see how to build from scratch a toy version of Cycle.js. See how to use RxJS to create simple web apps, and how to separate logic from effects.

Separate Logic from Effects in Cycle.js

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).

Find Clusters of Data with K-means Clustering in Python and Scikit-learn

WAI-ARIA, the Accessible Rich Internet Applications Suite, defines a way to make Web content and Web applications more accessible to people with disabilities. It especially helps with dynamic content and advanced user interface controls developed with Ajax, HTML, JavaScript, and related technologies. 

aria

Animations can make people sick, or worse! By adding animation toggles and listening in to the user's system preference for reducing motion on OSX and iOS, we can give them more control over our interfaces. Animation can be a safety issue; let's do something about it!

Warning: there is a flashing animation in the video.

In this lesson:
- [prefers-reduced-motion](https://webkit.org/blog/7551/responsive-design-for-motion/)

WCAG 2.1, Guideline 2.3: Seizure risks
- https://www.w3.org/WAI/WCAG21/quickref/?versions=2.0#qr-seizure-does-not-violate

Your Interactive Makes Me Sick
- https://source.opennews.org/articles/motion-sick/

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