Vibe coding: Your roadmap to becoming an AI developer 🤖
This article outlines a structured roadmap for transitioning from a novice coder to an AI developer using GitHub’s resources. It emphasizes mastering key programming languages (Python, Java, C++), AI frameworks (TensorFlow, PyTorch), and machine learning concepts (deep learning, NLP, computer vision). Additionally, it highlights the importance of building a GitHub portfolio and earning a GitHub Copilot certification to enhance employability.
2. Core Technical Concepts & Technologies
- Programming Languages: Python, Java, C++
- AI Frameworks & Libraries: TensorFlow, Keras, PyTorch, Scikit-learn
- Machine Learning Subfields: Deep Learning, NLP, Computer Vision
- GitHub Tools: GitHub Copilot, Learning Lab, OpenCV, NLTK
- Portfolio Development: GitHub Pages, README optimization, open-source contributions
3. Main Points
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Learn Essential Programming Languages & Frameworks
- Python dominates AI/ML due to its simplicity and rich libraries.
- Java and C++ are preferred for scalable and performance-critical applications.
- TensorFlow, PyTorch, and Scikit-learn are key frameworks for AI development.
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Master Machine Learning
- Deep learning powers complex tasks like speech recognition and
Learn how to go from curious coder to AI wizard—with a little help from GitHub.
This article was originally published on GitHub
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