Learn Chrome-devtools, Mongoose and Python

Miscellaneous one-off lessons on debugging, productivity hacks, and JavaScript tricks.

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.

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Tips and Tricks

Frontend runtime optimization is heavily impacted by rendering.  Understanding the way the JS engine and the browser work is crucial for optimizing applications and understading how to avoid performance pitfalls.
This collection explains about the Critical Rendering Path, performance bottlenecks and practical solutions.

Critical Rendering Path

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.

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Fully Connected Neural Networks with Keras

Chrome DevTools are incredibly useful for a frontend developer, sometimes we spend more time using them than our text editor of choice.

That's why it's so important to understand this tool better - those quick lessons will teach you things that will help you to become a more effective developer.

You will learn how to:

- **Use (experimental) CSS Overview to learn all about CSS used on a page in a minute**
- **Style console.log messages with CSS** - a _crucial_ skill for any senior developer (j/k)
- **Debug your code (on production and local environment) with logpoints**
- **Run a Lighthouse audit of your site**
- **Choose accessible colors for your website that have an AAA contrast ratio rating**
- **Force a DOM element state in Chrome DevTools**
- **Use Console utilities to make debugging easier**
- **Copy a network request as fetch or cURL with Chrome DevTools**

Have fun!

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

chrome-devtools

Chrome DevTools tips & tricks

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

In this course we’ll take a deep look at the Elements panel in the chrome devtools. We’ll step through each button and tab on this panel and discuss how to use them to interact with your web application as it runs.

Debug the DOM in Chrome with the Devtools Elements Panel

This course examines the Sources panel in the Chrome DevTools, which allows you to see what files are responsible for the various things on the screen. Bundled into this panel, though, is the single most useful tool for any JavaScript developer - the JavaScript Debugger. We’ll use the Debugger to inspect JavaScript as it runs, allowing you to watch (and even interact with) the state of your application as it runs in ways that’ll blow your mind if you’ve mostly used console.log to debug your applications to this point. 

Debug JavaScript in Chrome with DevTool Sources

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.

Up and Running with AWS Amplify Functions

This course will examine the Network panel in Chrome DevTools. We’ll step through the request log, examining the various properties exposed. We’ll talk about timing, filtering, throttling and generally using the network panel to better understand the ways in which our applications exist within a connected context.

Debug HTTP with Chrome DevTools Network Panel

If you are developing JavaScript applications you are likely using logging as an integral part of your development process. Did you know that you aren't limited to simple `console.log` statements? Modern browsers give you a lot more logging options to work with!

This course will explore those options and give you some new logging tools for your professional toolbox.

Advanced Logging with the JavaScript Console

This is a small collection of videos to help you get familiar with Chrome browser's performance oriented developer tools.
In this collection, learn how to record a performance profile, tweak the settings to get the most relevant profile and finally a look into the different sections of the performance data visualisation.

Basics of Chrome's Performance Devtools

Anything related to JavaScript performance

Javascript Performance

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

Misunderstanding scope can cause problems in your application. Watch this lesson to learn how Python scope works and the hidden implications it presents. Local scope, nonlocal scope, and global scope variables are demonstrated and explained.

Understand Scope in Python

Get more mileage from your console output by going beyond mere string logging - log entire introspectable objects, log multiple items in one call, and apply C-Style string substitution to make the console work for you.

Advanced Console Log Arguments

A module is a function extracted to a file. This allows you to import the function and use it in any other code you may write. You’ll learn how to create modules, import them, and make them stand-alone as you learn what `if __name__ == “__main__”` means in Python.

Reuse Code in Multiple Projects with Python Modules

Why should you have to write all the code? Why can’t you leverage code written by others? I’ve got great news: you can. You’ll learn how to search pip, Python’s package installer, to find and install packages to add new features and functionality to your application.

Manage packages with pip in Python

Object oriented classes work much like classes in other languages. Learn how to create them and use them, learn the difference between class variables and instance variables, creating class methods, and learn how to create classes that inherit from other classes.

Use Python Classes

Organize your log output by grouping your logs into collapsable hierarchies using console.group().

Grouping and Nesting Console Log Output

Getting input from the user via a console command can be done with `input` in Python. Learn how to implement it and validate user input in this lesson.

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