Vibe coding: Your roadmap to becoming an AI developer

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Pop quiz: What do healthcare, self-driving cars, and your next job all have in common? 

If you guessed AI, you were right. And with 80% of developers expected to need at least a fundamental AI skill set by 2027, there’s never been a better time to dive into this field.

This blog will walk you through what you need to know, learn, and build to jump into the world of AI—using the tools and resources you already use on GitHub. 

Let’s dive in.

1. Learn essential programming languages and frameworks 💬

Mastering the right programming languages and tools is foundational for anyone looking to excel in AI and machine learning development. Here’s a breakdown of the core programming languages to zero in on:

• Python: Known for its simplicity and extensive library support, Python is the cornerstone of AI and machine learning. Its versatility makes it the preferred language for everything from data preprocessing to deploying AI models. (Fun fact: Python overtook JavaScript as the number one programming language in 2024!)
• Java: With its scalability and cross-platform capabilities, Java is popular for enterprise-level applications and large-scale AI systems.
• C++: As one of the fastest programming languages, C++ is often used in performance-critical applications like gaming AI, real-time simulations, and robotics.

Beyond programming, these frameworks give you the tools to design, train, and deploy intelligent systems across real-world applications:

• TensorFlow: Developed by Google, TensorFlow is a comprehensive framework that simplifies the process of building, training, and deploying AI models.
• Keras: Built on top of TensorFlow, Keras is user-friendly and enables quick prototyping.
• PyTorch: Favored by researchers for its flexibility, PyTorch provides dynamic computation graphs and intuitive debugging tools.
• Scikit-learn: Ideal for traditional machine learning algorithms, Scikit-learn offers efficient tools for data analysis and modeling.

Spoiler alert: Did you know you can learn programming languages and AI frameworks right on GitHub? Resources like GitHub Learning Lab, The Algorithms, TensorFlow Tutorials, and PyTorch Examples provide hands-on opportunities to build your skills. Plus, tools like GitHub Copilot provide real-time coding assistance that can help you navigate new languages and frameworks easily while you get up to speed.

 2. Master machine learning 🤖

Machine learning (ML) is the driving force behind modern AI, enabling systems to learn from data and improve their performance over time. It bridges the gap between raw data and actionable insights, making ML expertise a must-have if you’re looking for a job in tech. Here are some key subfields to explore:

• Deep learning: A subset of ML, deep learning uses multi-layered neural networks to analyze complex patterns in large datasets. While neural networks are used across ML, deep learning focuses on deeper architectures and powers advancements like speech recognition, autonomous vehicles, and generative AI models.
• Natural language processing (NLP): NLP enables machines to understand, interpret, and respond to human language. Applications include chatbots, sentiment analysis, and language translation tools like Google Translate.
• Computer vision: This field focuses on enabling machines to process and interpret visual information from the world, such as recognizing objects, analyzing images, and even driving cars.

Luckily, you can explore ML right on GitHub. Start with open source repositories like Awesome Machine Learning for curated tools and tutorials, Keras for deep learning projects, NLTK for natural language processing, and OpenCV for computer vision. Additionally, try real-world challenges by searching for Kaggle competition solutions on GitHub or contribute to open source AI projects tagged with “good first issue” to gain hands-on experience. 

3. Build a GitHub portfolio to showcase your skills 💼

A strong GitHub portfolio highlights your skills and AI projects, setting you apart in the developer community. Here’s how to optimize yours:

• Organize your repositories: Use clear names, detailed README files, and instructions for others to replicate your work.
• Feature your best work: Showcase projects in areas like NLP or computer vision, and use tags to improve discoverability.
• Create a profile README: Introduce yourself with a professional README that includes your interests, skills, and standout projects.
• Use GitHub Pages: Build a personal site to host your projects, case studies, or interactive demos.
• Contribute to open source: Highlight your open source contributions to show your collaboration and technical expertise.

For detailed guidance, check out the guides on Building Your Stunning GitHub Portfolio and How to Create a GitHub Portfolio.

4. Get certified in GitHub Copilot 🏅

Earning a certification in GitHub Copilot showcases your expertise in leveraging AI-powered tools to enhance development workflows. It’s a valuable credential that demonstrates your skills to employers, collaborators, and the broader developer community. Here’s how to get started:

• Understand GitHub Copilot: GitHub Copilot is an AI agent designed to help you write code faster and more efficiently. Familiarize yourself with its features, such as real-time code suggestions, agent mode in Visual Studio Code, model context protocol (MCP), and generating boilerplate code across multiple programming languages.
• Explore certification options: GitHub offers certification programs through its certification portal. These programs validate your ability to use GitHub tools effectively, including GitHub Copilot. They also cover key topics like AI-powered development, workflow automation, and integration with CI/CD pipelines.
• Prepare for the exam: Certification exams typically include theoretical and practical components. Prepare by exploring GitHub Copilot’s official documentation, completing hands-on exercises, and working on real-world projects where you utilize GitHub Copilot to solve coding challenges.
• Earn the badge: Once you complete the exam successfully, you’ll receive a digital badge that you can showcase on LinkedIn, your GitHub profile, or your personal portfolio. This certification will enhance your resume and signal to employers that you’re equipped with cutting-edge AI development tools.

Check out this LinkedIn guide for tips on becoming a certified code champion with GitHub Copilot. 

Written by

Gwen Davis

@purpledragon85

Gwen Davis is a senior content strategist at GitHub, where she writes about developer experience, AI-powered workflows, and career growth in tech.