Learn Grep, Karma and Python

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

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

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Introductory Machine Learning Algorithms in Python with scikit-learn

CMD+F in your text editor can only get you so far! In this course, we will learn to use grep and find, two powerful command-line utilities, to search through and explore our codebase. Learn to search specific files and folders, using regular expressions to look for -- or ignore! -- particular phrases. We'll cover how to search recursively, get more context on your results, and some simple tricks to make your searches faster and more targeted.


Once you're familiar with grep and find, you'll be able to quickly answer questions such as: Do I use this function anywhere other than my tests? Where does this file get imported? Whether you're working on a new feature, reading a codebase for the first time, or refactoring existing code, grep and find can help you work faster and more effectively.

A Unix command used to search files for the occurrence of a string of characters that matches a specified pattern.

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Use Grep for Fast Search from the Command Line

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.

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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

When you have a bunch of text to sift through, standard Unix command line utilities like `grep`, `sed`, `awk`, `cut`, `sort`, and `uniq` can help extract the information you want. For example, with a git log for a project, you might want to group commits by day in order to see which days you're most productive, or find out how many commits you made last year. In this lesson, you'll learn how to combine these commands with the pipe operator to extract this data from git logs.

Analyze Git Logs with the Command Line

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

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

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

Sometimes you'll be looking for a string, but won't know which file it's in; or you'll want to find all usages of it within a directory. Learn how to use grep -r to recursively search all the files in a directory. 

Search directory contents recursively using grep

Learn how to use find to identify filenames matching specified patterns. We'll use find to identify all .jsx files in a directory, or all test files in a directory.

Use find to search for filename patterns

Learn to use git grep to only search through the tracked files in a git repo. This is especially useful when you want to exclude build artifacts or locally installed dependencies, such as webpack bundles or the node_modules directory. You'll also note that git grep is automatically colorized - we'll see how to get the same coloring effect with grep --color.

Search the contents of a git repository with git grep

By default, grep displays the file path and the line where a match is found. Learn to use the flags -A, -B, -C, and -n to get more context, and view line numbers as well as the lines surrounding a pattern match.


Show context around matches using grep

By using wildcards and glob patterns with the special characters . and *, you can create regular expressions that describe what kind of text you're looking for, instead of just plaintext strings. 

Search for basic patterns using grep

Learn to use grep's extended regular expressions to describe more complex patterns. The `?` and `+` special characters describe optional patterns. The `?` character matches zero or one instance of the preceding term, and the `+` character matches one or more instances of the preceding term. To use these characters, you'll need to either escape them with backslashes or turn on extended regular expressions with the `-E` flag.

Use grep's extended regular expressions to find complex patterns

Learn to use the special anchor characters `^` and `$` to indicate the beginning and end of lines when writing regular expressions for grep. These line anchors are part of basic regular expressions in grep, so you don't need to escape them.

Specify line beginning and end in patterns using grep

You can describe classes of characters using bracket expressions. We'll use expressions such as [a-zA-Z] and [0-9] to search for alphabetic and numeric characters with grep.

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