Learn Particle, 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

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

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

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

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

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

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

JetBrains IDEs have a very good code completion for JavaScript code but you can do it even better by adding TypeScript typings, even if you don’t use TypeScript in your project. Should work in WebStorm, PhpStorm, RubyMine, PyCharm and others.

Improve JavaScript Code Completion in WebStorm with TypeScript Typings

particle

Before powering up the Particle Photon we are going to look at what each of the components are on the board.

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