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[Reset] Clean up structure, get ready for recipes
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{ | ||
// Default codespaces-jupyter configuration | ||
// See: https://github.com/github/codespaces-jupyter | ||
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"updateContentCommand": "python3 -m pip install -r requirements.txt", | ||
"postCreateCommand": "", | ||
// Default codespaces-jupyter configuration | ||
// See: https://github.com/github/codespaces-jupyter | ||
"name": "#30DaysOfPython", | ||
"image": "mcr.microsoft.com/devcontainers/universal:2", | ||
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"hostRequirements": { | ||
"cpus": 4 | ||
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"updateContentCommand": "python3 -m pip install -r requirements.txt", | ||
// "postCreateCommand": "cd docs && npm install && npm run dev", | ||
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"codespaces": { | ||
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"ms-toolsai.jupyter", | ||
"ms-python.python" | ||
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.env |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Python Basics: Lab 01\n", | ||
"\n", | ||
"Exercises will cover basic Python syntax, data structures, and functions. Open the Outline view in Visual Studio Code to see the syllabus at a glance. Each exercise should take under 5 minutes to complete. Useful resources for reference include:\n", | ||
"\n", | ||
"1. [Kaggle Python Course](https://www.kaggle.com/learn/python) - short, task-driven\n", | ||
"1. [Foundations of Python Programming](https://runestone.academy/ns/books/published/fopp/index.html) - long, detail-driven\n", | ||
"1. [Learn Python The Hard Way](https://learnpythonthehardway.org/python3/) - online, exercises-driven" | ||
] | ||
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"name": "python" | ||
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--- | ||
title: Table Of Contents | ||
description: Start by learning Python3 syntax and basics | ||
--- | ||
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The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup. | ||
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|Lab Notebook | Recipe Notes | | ||
|---|---| | ||
| [Lab 01](./lab-01.ipynb) | Learn Basic Python Syntax, Get Familiar with Notebooks. | | ||
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--- | ||
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## Resources | ||
I wanted to start with a structured set of courses or modules that would have an associated community (for support) and a credential (for achievement). So I decided to focus on the [Python 3 Programming Specialization](https://www.coursera.org/specializations/python-3-programming) offered by the University Of Michigan, on Coursera. | ||
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It consists of these 5 courses _with an estimated completion time of 3 months at 10 hrs/week_. I expect to complete at least 2-of-5 courses in #30Days. | ||
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1. [Python Basics](https://www.coursera.org/learn/python-basics?specialization=python-3-programming) Course - 34 hours, 4 modules | ||
1. [Python Functions, Files & Dictionaries](https://www.coursera.org/learn/python-functions-files-dictionaries?specialization=python-3-programming) Course - 31 hours, 5 modules | ||
1. [Data Collection & Processing With Python](https://www.coursera.org/learn/data-collection-processing-python?specialization=python-3-programming) - 16 hours, 3 modules | ||
1. [Python Classes & Inheritance](https://www.coursera.org/learn/python-classes-inheritance?specialization=python-3-programming) - 18 hours, 3 modules | ||
1. [Python Project: pillow, tesseract, opencv](https://www.coursera.org/learn/python-project?specialization=python-3-programming) - 21 hours, 3 modules | ||
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Once this specialization is complete, I'll continue to add new sections focused on _applied learning_ exploring the usage of Python3 in specific application contexts like **data science**, **generative AI** and **web app development**. | ||
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--- | ||
title: Table Of Contents | ||
description: Learn to use Python3 packages and tools for data analysis and visualization | ||
--- | ||
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The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup. | ||
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|Lab Notebook | Recipe Notes | | ||
|---|---| | ||
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--- | ||
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## Resources | ||
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:::tip[PyDataNYC 2023 Talk] | ||
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To learn more about why these 4 tools matter, check outmy talk on _Simplifying Data Analysis with GitHub Codespaces, Jupyter Notebooks & Open AI_ at the PyDataNYC conference in Nov 2023. | ||
- Download: [PyDataNYC Presentation](https://speakerdeck.com/nitya/simplifying-data-analysis-with-github-codespaces-jupyter-notebooks-and-open-ai). | ||
- Watch: [The Python Pulse @PyDataNYC](https://www.youtube.com/watch?v=SiiVN7kwOZI). | ||
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::: |
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--- | ||
title: 1. Introduction | ||
description: Understand how to build machine learning models | ||
--- | ||
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The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup. | ||
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|Lab Notebook | Recipe Notes | | ||
|---|---| | ||
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--- | ||
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## Resources |
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--- | ||
title: 1. Introduction | ||
description: Explore Python SDKs and tools for building generative AI applications | ||
--- | ||
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The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup. | ||
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|Lab Notebook | Recipe Notes | | ||
|---|---| | ||
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--- | ||
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## Resources |
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--- | ||
title: "#30DaysOf Python" | ||
description: Hands-on with Python - Core Concepts to AI/ML Apps. | ||
description: Python Hands-On - Basics to AI | ||
template: splash | ||
hero: | ||
tagline: Python3 - from Core Concepts to AI Applications | ||
tagline: Get Cooking With Python3 - Try One Recipe A Day! | ||
image: | ||
file: ../../assets/pair-python.svg | ||
file: ../../assets/logo.png | ||
actions: | ||
- text: Start Learning | ||
link: introduction/ | ||
icon: right-arrow | ||
variant: secondary | ||
--- | ||
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## Why Python? | ||
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There has never been a better time to learn Python (as a beginner) or upskill in Python (as an experienced developer). | ||
- It ranks #1 on the [TIOBE Index](https://www.tiobe.com/tiobe-index/). This rates programming languages based on usage (lines of code written). | ||
- It is general purpose. Use it for data science, automation, web development, testing and more. | ||
- It is a core skill for AI/ML. Most Large Language Models and Generative AI services release Python SDKs first. | ||
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## Learning Goals: | ||
- Learn Python from core concepts (language) to advanced applications (AI/ML) - with code! | ||
- Setup and use a consistent and reproducible envirionment (Dev containers, Jupyter Notebooks) | ||
- Cultivate curiosity in learning without losing focus or context (GitHub Copilot) | ||
--- | ||
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_Image Credit: [undraw.co](https://undraw.co)_ | ||
_Logo By [Copilot Designer](https://www.bing.com/images/create)_ | "abstract 3D shape python code logo, computers and bubbles incorporated, lines of code emitted, black background" |
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