Learn Babel, Scikit-learn and Webstorm

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

Babel has support for the latest version of JavaScript through syntax transformers. These plugins allow you to use new syntax, right now without waiting for browser support.

babel

Converting all of our modern JavaScript into ES5 compatible syntax is a great way to use modern features while targeting older browsers. What happens when the browsers natively support these language features? Then it no longer makes sense to transform that code or to include polyfills that will go unused. In this lesson, we’ll add the `@babel/polyfill` package and configure `babel-preset-env`

Target specific browsers with babel-preset-env and the babel pollyfill

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

We take the opengraph image we wrote in codesandbox and copy it into our serverless function. We use Rollup to compile the component into an IIFE (immediately invoked function expression) that can run in our headless browser environment and cover a wide set of rollup plugins that we need for compatibility. We end the lesson having successfully screenshot our opengraph react component from our Netlify Function

Compiling a React to an IIFE with Rollup to execute and render in a headless browser

When exploring a new framework or technology, it is nice to have a seed project or sandbox so you can just get in and start playing. Let's build our React playground using npm, browserify, 6to5, and more within WebStorm!

Setting up a React Playground Dev Environment

We can write our package using the latest JavaScript syntax with a compilation step. In this lesson we will install `babel-cli` and `babel-preset-latest` and wire up a `build` script to compile our code using the babel “latest” preset when source files change. This means those using our package can use it in any version of JavaScript even though we are using the latest syntax for our source code.

Set up compilation of source code using babel with npm scripts

Lightweight yet powerful IDE, perfectly equipped for complex client-side development and server-side development with Node.js.

webstorm

John Lindquist is a WebStorm wizard. In this lesson, he'll show you some of the tips & tricks he uses to squeeze maximum productivity out of his tool of choice. 

Tips & Tricks

You can easily share your code from WebStorm with others using the "Create Gist" feature. This lesson walks you through how to create a gist from a single or multiples files and also how to use gist.run to share a running version of your code

WebStorm - Sharing a Gist

WebStorm can save you tons of time by managing your imports for you. John walks you through a few key WebStorm importing tips to help speed up your importing workflow.

WebStorm - Managing Imports

A quick tutorial covering installing Karma and running tests from the command line and Webstorm.

Introduction to Karma

Gulp is a toolkit for automating painful or time-consuming tasks in your development workflow, so you can stop messing around and build something.

gulp

What is already a nice workflow using Gulp, Browserify, Watchify & Browsersync to enable auto rebuilds & live browser reloading can be further improved by adding Babelify. A simple wrapper around the popular transpiler Babel, Babelify can ‘transform’ source files as they pass through our stream to enable certain Javascript features not yet available in the Browser or Node. We end the lesson by looking at how to generate & extract sourcemaps using `exorcist`.

Gulp and Browserify - Adding Babel & Source Maps

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

In this lesson John shows some tricks for speeding up your test suite. He digs into some handy features of WebStorm, including the *ddescriber* plugin, as well as some alternative methods for Jasmine for selectively running your tests.

Speed Testing with WebStorm and Jasmine

In this lesson, we'll walk through installing a custom color scheme into our [WebStorm](https://www.jetbrains.com/webstorm/) IDE. Dayle Rees's [daylerees/colour-schemes](https://github.com/daylerees/colour-schemes/blob/master/jetbrains/peacocks-in-space.icls) has a great collection of themes ready to install in different formats for various editors.

For this example, we will install the [Peacocks In Space](https://github.com/daylerees/colour-schemes/blob/master/jetbrains/peacocks-in-space.icls) theme into an OS X [WebStorm 11 EAP](https://confluence.jetbrains.com/display/WI/WebStorm+EAP) installation. _Note: This process will be very similar (or identical) for previous versions of WebStorm and other editors in the [JetBrain's family of editors](https://www.jetbrains.com/)._

WebStorm - Installing Custom Color Schemes (dayleress/colour-schemes)

WebStorm can easily help you set up a new Angular 2 project with never even touching a terminal. This lesson shows you all the neat GUI tricks you need to know to get up and running.

WebStorm - Setting Up Angular 2

Babel is a compiler that lets us right future JavaScript not yet fully in browsers (ES6 / ES2015, ES7 / ES2016, etc) and transpile it back into code that can be used now. We'll walk through installing babel, writing some future JS syntax, transpiling it, and running it in a browser.

**This lesson is out of date and uses an older version of Babel.**

Writing next generation JavaScript with Babel 5 (out of date)

grunt

In this lesson you will install GruntJS and get up and running with your first task, initial configuration, and how to get Grunt methods auto-completing in WebStorm.

Introduction to Grunt

Webpack loaders are how you tell webpack to resolve your dependencies and you can use a loader built for the babel transpiler to easily enable ES6 code in your project. Full sourcemap support is also available using webpack's devtool property.

Webpack Loaders, Source Maps, and ES6

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`.

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