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lauragift21/awesome-learning-resources

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TLDR

A community-curated collection of learning links covering web development, programming languages, DevOps, machine learning, and career topics, organized by subject so you can jump straight to what you want to learn next.

Mindmap

mindmap
  root((awesome-learning))
    Web Dev
      HTML and CSS
      JavaScript
      React Vue Angular
      Svelte
    Languages
      Python Java Go
      Rust TypeScript
      Ruby Kotlin PHP
    Tools and Practices
      Git Linux
      DevOps Serverless
      Agile
    ML and AI
      Machine Learning
      Deep Learning
      TensorFlow
      Computer Vision
    Career
      Developer Blogs
      Podcasts
      Women in Tech
      Career Advice
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

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Things people build with this

USE CASE 1

Find beginner-friendly tutorials when picking up a new programming language or framework for the first time.

USE CASE 2

Discover free courses, docs, and blog posts to level up your web development skills without paying for a bootcamp.

USE CASE 3

Explore career advice, podcasts, and community resources to grow as a developer beyond just coding.

USE CASE 4

Use it as a bookmark hub to share learning links with teammates or students getting started in tech.

Tech stack

HTMLCSSJavaScriptPythonReactTypeScriptGoRust

Getting it running

Difficulty · easy Time to first run · 5min

No installation needed. Open the README on GitHub and browse sections by topic. Click any link to go directly to the resource.

No license is mentioned for this repository.

In plain English

This repository is a curated list of learning resources for web development and related technical topics. It is organized as a long reference document where each section covers a different technology or subject area, with links to tutorials, official documentation, blog posts, courses, and other materials that the maintainer has found useful. The topics covered are broad. On the web development side, there are sections for HTML, CSS, JavaScript, and popular frameworks like React, Vue, Angular, and Svelte. On the language side, you will find resources for Python, Java, Go, Ruby, Rust, TypeScript, Kotlin, PHP, and others. The list also covers tools and practices like Git, Linux, DevOps, serverless computing, and Agile methodology. There are sections for machine learning, deep learning, TensorFlow, and computer vision. Softer topics like career advice, developer blogs, developer stories, podcasts, and women in tech resources are also included. Each entry in a section is typically a single link with a short label, sometimes with a brief note. The links point to a mix of free and paid resources, including sites like freeCodeCamp, MDN, W3Schools, Udemy, YouTube channels, and individual developer blogs. There is no rating system or description of quality differences between entries. This kind of repository is commonly called an "awesome list," a format popularized on GitHub where contributors submit links organized by topic. Anyone can suggest additions via a pull request. The list is not specific to one programming language or framework, making it a starting point for exploring a new area rather than a deep guide to any single subject. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
I want to learn React from scratch. Based on the lauragift21/awesome-learning-resources list, which types of resources are included for React and how should I sequence them as a beginner?
Prompt 2
I am a non-technical founder who wants to understand what DevOps means. Summarize what the DevOps section of this awesome list covers in plain English.
Prompt 3
Using the resources in lauragift21/awesome-learning-resources, suggest a 30-day self-study plan for someone who knows basic HTML and wants to become job-ready in JavaScript.
Prompt 4
Which machine learning resources in this list are suitable for someone with no prior ML experience but some Python knowledge?
Prompt 5
I want to contribute a new link to this awesome list. What format and guidelines should I follow based on how existing entries are structured?
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