What’s the Best Way to Learn Coding Once You’re Over with Tutorials?
After you’ve worked through coding tutorials and built your foundational skills, the next step is to immerse yourself in real-world codebases. Tutorials provide controlled environments with step-by-step guidance, but actual projects introduce complexity and variability that bring invaluable learning opportunities.
Diving into codebases, particularly open-source projects, gives you the chance to see how seasoned developers tackle real challenges, structure large projects, and solve issues collaboratively.
Contributing to open-source projects helps you grow technically and professionally, enhancing your coding skills in real-world environments.
- You gain exposure to best practices in version control, code review, and project management, valuable skills for any developer.
- Open-source contributions allow diverse ways to engage, from fixing bugs and enhancing documentation to building new features.
- Each experience provides hands-on learning, allowing you to understand complex projects, new tools, and methods beyond standard tutorials.
Here Are Some Repositories You Can Explore in Python to Develop Skills Rapidly
SWIRL
SWIRL is an open-source AI infrastructure software that powers Search & Retrieval Augmented Generation (RAG) applications. It simplifies and enhances AI pipelines by seamlessly integrating large language models (LLMs) with various data sources like GitHub, Slack, Teams, Outlook & 100 more.
Contribute to SWIRL if you want to learn about:
- End-to-End Applications in Python, Django
- AI & AI Calls to GPT & More
- Apps & Database Integrations
Tech Stack: Python, Django
Pytest
Pytest is a testing framework that makes it easy to write simple and scalable test cases for Python applications. It supports fixtures, parameterized testing, and a variety of plugins to extend its functionality.
Tech Stack: Python
DocsGPT
DocsGPT is a chatbot designed for documentation, allowing users to interact with their data. It is privately deployable, provides AI knowledge sharing, and integrates seamlessly into AI workflows.
Tech Stack: Python, FastAPI, LangChain, React, TypeScript
CopilotKit
CopilotKit offers React UI components and infrastructure for integrating AI copilots, in-app AI agents, chatbots, and AI-powered text areas into applications. It provides a streamlined approach to embedding AI functionalities within React applications.
Tech Stack: React, TypeScript, Node.js
Taipy
Taipy is designed for data scientists and machine learning engineers to build data and AI web applications. It enables the creation of production-ready web applications using only Python, allowing users to focus on data and AI algorithms without the complexities of development and deployment.
Tech Stack: Python
Fastapi
FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints. It is easy to learn, fast to code, and ready for production, offering automatic interactive API documentation.
Tech Stack: Python, Pydantic
Reflex
Reflex allows developers to build web applications entirely in Python, eliminating the need to write JavaScript or HTML. It offers a framework for creating interactive web apps with a focus on simplicity and performance.
Tech Stack: Python
AI For Beginners by Microsoft
AI-For-Beginners is a 12-week, 24-lesson curriculum designed to introduce learners to artificial intelligence concepts. It includes practical lessons, quizzes, and labs, covering tools like TensorFlow and PyTorch, and is suitable for beginners.
⭐️ AI For Beginners on GitHub.
Tech Stack: Python, Jupyter Notebooks, TensorFlow, PyTorch
MONAI
MONAI (Medical Open Network for AI) is a PyTorch-based, open-source framework tailored for deep learning in healthcare imaging. It offers domain-optimized tools and workflows, facilitating the development and evaluation of AI models in medical imaging. MONAI supports tasks such as image segmentation, classification, and registration, providing researchers and clinicians with a standardized platform to advance medical AI applications.
Tech Stack: Python, PyTorch
Some Extra Resources:
Since I’m talking about learning, if you’re applying to jobs I think these repositories might be helpful:
Resume Matcher
Resume Matcher is an open-source, AI-based tool designed to enhance your resume by aligning it with specific job descriptions. It identifies matching keywords, improves readability, and provides in-depth insights to make your resume more ATS-friendly.
Tech Stack: Python, Natural Language Processing (NLP), Streamlit
New Grad Positions
This repository compiles entry-level software, tech, CS, PM, and quant job opportunities for 2024 and 2025 new graduates. It serves as a collaborative platform to share and track job openings in the United States, Canada, and remote positions.
⭐️ New-Grad-Positions on GitHub.
Tech Stack: Markdown, GitHub Pages
Machine Learning Collection
This repository offers a curated collection of machine learning technologies developed by Microsoft and its subsidiaries. It includes libraries, tools, sample codes, and workshop content, providing valuable resources for both beginners and experienced practitioners in the field of machine learning.
⭐️ machine-learning-collection on GitHub.
Tech Stack: Python, Jupyter Notebooks, Various Machine Learning Frameworks
Okay, time to go and study. A big thank you to you, mate! For reading and supporting. 💜 💖 💛
Top comments (4)
Great share, thanks for mentioning DocsGPT 🚀
DocsGPT is great! 🔥
Thanks for mentioning Resume Matcher. Could use some help over there
: )
You're welcome!