Learn Redwoodjs, 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

RedwoodJS takes the JAMstack philosophy to the next level. It brings together all the tools modern web development has to offer:

- React
- GraphQL
- Prisma

RedwoodJS abstracts database management away from you so that you can just build your schema and it will do all the plumbing. This collection will go over how to create a RedwoodJS project and the scripts that will help make your developer experience a joy.


Bringing full-stack to the JAMstack

Do you love the JAMstack philosophy but need a database-backed web app? Want great developer experience and easy scaling? Redwood is here! Built on React, GraphQL, and Prisma, Redwood works with the components and development workflow you love, but with simple conventions and helpers to make your experience even better.

redwoodjs

Introduction to RedwoodJS: full-stack framework for JAMstack

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 use `yarn redwood generate page` (`yarn rw` for short) in this lesson to generate an `IndexPage` component. 

The first argument to `yarn rw generate page` is the name of the page you are generating. The second argument is the path you want the page to show up on. So `yarn rw generate page index /` will generate an `IndexPage` component and insert a route entry in our `web/src/Routes.js` file.

We learn that redwood import `React` for you so you don't have to import it manually when you are creating a component.

Generate a Page Using the Redwood CLI

In this lesson, we are going over how to generate a `RedwoodJS` project.

We can create a `RedwoodJS` project with `yarn create redwood-app ./my-redwood-project`.

`yarn` will generate the `RedwoodJS` app in the file folder you specified without installing any globals.

To run the `RedwoodJS` app, we use `yarn redwood dev`. This runs the Redwood client as well as the `GraphQL` backend.

Generate a Redwood Project

RedwoodJS provides a `Link` component that we can use to navigate around our app. We can provide a `to` prop with the path that we want to navigate to.

RedwoodJS takes this a step further and provides a `routes` object from `@redwoodjs/router`. This object is aware of all the routes defined in `web/Routes.js`. This allows us to use the methods on this object rather than referencing the our routes with a string literal. We can then change the route path without having to change all of the references to that route all over our app.

Use RedwoodJS Link and routes Object to Navigate

RedwoodJS gives us a cli command to generate layouts.

`yarn redwood generate layout main`

This will generate a `MainLayout` component in `src/layouts/MainLayout/`. We can use this layout component to define any headers or footers we want to share across multiple pages. 

Extract a RedwoodJS Layout Component

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).

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