Learn Bower, Mongodb and Python

MongoDB has a quite powerful aggregation framework that allows you to write data processing pipelines. Documents in the MongoDB collection go through a series of stages or a pipeline that transforms them into an aggregated result. In this short video series, you will learn how to write aggregations in MongoDB.

mongodb

MongoDB Aggregation Framework

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

python

Fully Connected Neural Networks with Keras

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

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

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Up and Running with AWS Amplify Functions

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Sometimes you get strings instead of objects and you have to parse them out. Here's an example where the raw data I got from a Twitch chatbot message wasn't useful to me until I parsed it into a dictionary.

Turning a key/value string into a dictionary in Python

Aggregation in MongoDB is really powerful and allows you to create solid pipelines to extract and transform data. This is an introductory video that aims to demonstrate the basics of aggregation — we will see how to create some basic pipeline to extract data using `$match` and manipulate the output using `$project` stages.

The aggregation stages/operators covered in this video are:

* [`$match`](https://docs.mongodb.com/manual/reference/operator/aggregation/match/index.html)
* [`$project`](https://docs.mongodb.com/manual/reference/operator/aggregation/project/index.html)
* [`$concat`](https://docs.mongodb.com/manual/reference/operator/aggregation/concat/)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/)


Find and Manipulate Data using Aggregation in MongoDB

In our last lesson, we learnt the basics of MongoDB aggregation and saw how we can [find and manipulate data](https://egghead.io/lessons/mongodb-find-and-manipulate-data-using-aggregation-in-mongodb). In this continuation, we will see how we can sort, skip and limit records during our aggregation stages.

The aggregation stages/operators covered in this video are:

* [`$sort`](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)
* [`$skip`](https://docs.mongodb.com/manual/reference/operator/aggregation/skip/index.html)
* [`$limit`](https://docs.mongodb.com/manual/reference/operator/aggregation/limit/index.html)

You can see the list of all the [available operators in the MongoDB docs.](https://docs.mongodb.com/manual/reference/operator/aggregation/)

Sort, Skip and Limit Records in MongoDB Aggregation

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

Read from a File in Python

Mongoose provides a straight-forward, schema-based solution to model your application data. It includes built-in type casting, validation, query building, business logic hooks and more, out of the box.

mongoose

One of the great things about NoSQL databases is that you do not have to decide on your data schema while developing. As you get closer to production though, you'll often want to more clearly define the shape of the data you are working with.

Enforce Schemas in MongoDB using Mongoose Models

Unlike the relational databases, MongoDB doesn't allow you to join collections using the simple `find` queries, but you can write the join queries using the aggregation framework. In this lesson, we learn how to join two collections to get the data in the format we want.

The aggregation operators that we cover in this video are:

- [`$lookup`](https://docs.mongodb.com/manual/reference/operator/aggregation/lookup/index.html)
- [`$project`](https://docs.mongodb.com/manual/reference/operator/aggregation/project/index.html)
- [`$arrayElemAt`](https://docs.mongodb.com/manual/reference/operator/aggregation/arrayElemAt/index.html)

You can see the list of all the [available operators in the MongoDB docs.](https://docs.mongodb.com/manual/reference/operator/aggregation/)

Write Joins in MongoDB

[In our last lesson](https://egghead.io/lessons/mongodb-group-by-and-sum-in-mongodb?pl=mongodb-aggregation-framework-3e5d), we saw how we can use `$group` operator to simulate `GROUP BY` operations that you might be aware from SQL. A minor gimmick with `$group` is that it only works with the scalar field values and not arrays. There might be times when you would want to group by some array fields; in this lesson, we learn how to do that using `$unwind` operator

Here is the list of operators that we cover in this lesson:

- [$unwind](https://docs.mongodb.com/manual/reference/operator/aggregation/unwind/index.html)
- [$group](https://docs.mongodb.com/manual/reference/operator/aggregation/group/index.html)
- [$sort](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)
- [$limit](https://docs.mongodb.com/manual/reference/operator/aggregation/limit/index.html)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/).


Perform Group Operations on Array Fields in MongoDB

In this lesson, we learn how to group documents in a collection using `$group` operator similar to `GROUP BY` in SQL. We also look at `$sum` operator and see how we can use it to count the number of documents as well as get sum of some field in our grouped results.

Here is the list of operators that we cover in this lesson:

* [`$group`](https://docs.mongodb.com/manual/reference/operator/aggregation/group/index.html)
* [`$sum`](https://docs.mongodb.com/manual/reference/operator/aggregation/sum/index.html)
* [`$sort`](https://docs.mongodb.com/manual/reference/operator/aggregation/sort/index.html)

You can see the list of all the [available operators in the MongoDB docs](https://docs.mongodb.com/manual/reference/operator/aggregation/).


Group By and Sum in MongoDB

Learn how to create a tuple in Python. A tuple is an immutable data type that contains several items similar to a list. Items in a tuple can be of any data type.

Create a Tuple in Python

Learn to create a one item tuple. When creating a one item tuple, a comma has to be included after the item. Otherwise, your tuple will not return tuple as its data type. 

Create a One Item Tuple in Python

Tuples are immutable so there is no way to add or replace items in tuples. There is a way to get around this by adding two tuples into one. 

Understand Immutability with Python Tuples

Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications.

express

Mongoose allows you to easily select resources by ID from your MongoDB. This is an important aspect to creating an API.

Select by ID with Mongoose and Express

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

node

Learn how to import data into your MongoDB and then use Express to serve a simple Node.js API.

First API with Node.js, Express and MongoDB

bower

Bower is a package manager for Javascript libraries that allows you to define, version, and retrieve your dependencies. In this tutorial, John gives a quick introduction to Bower and will show you how to get started.

Topics

Instructors

Content Type