Prerequisites & Background
While you don't need to be an expert to start, having some foundational knowledge will help you progress faster:
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Programming Fundamentals
Basic proficiency in Python or another programming language. Understanding of variables, functions, loops, and data structures.
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Mathematics Basics
High school level mathematics including algebra and basic statistics. Linear algebra and calculus are helpful but not required to start.
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Computer Science Concepts
Understanding of algorithms, data structures, and basic software engineering principles.
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Command Line & Git
Comfort with terminal/command line operations and version control using Git.
Recommended Learning Path
Follow this structured approach to maximize your learning:
Weeks 1-4: Foundations
Start with Module 1 to understand AI history and fundamentals. Focus on classical ML concepts before diving into deep learning.
Weeks 5-8: Tools & Frameworks
Explore Module 2 to get hands-on with modern AI tools. Learn Hugging Face, LangChain, and prompt engineering basics.
Weeks 9-12: Practical Applications
Work through Module 3 for enterprise use cases. Build your first RAG application and understand real-world implementations.
Weeks 13-16: Production & Deployment
Master Modules 4-5 for infrastructure and security. Learn to deploy models and ensure compliance.
Ongoing: Projects & Specialization
Complete Module 7 projects to build your portfolio. Choose a specialization area and dive deep.
Environment Setup
Get your development environment ready with these essential tools:
Your First Steps Checklist
Complete these steps to ensure you're ready to begin:
- Install Python 3.8+ and verify installation with
python --version - Set up a virtual environment using venv or conda
- Install essential packages: numpy, pandas, scikit-learn, torch/tensorflow
- Create a GitHub account and set up Git locally
- Complete a "Hello World" in Jupyter Notebook
- Join AI communities (Discord, Reddit r/MachineLearning, etc.)
- Bookmark key documentation sites (PyTorch, TensorFlow, Hugging Face)
- Set up a learning schedule (recommend 1-2 hours daily)
- Choose your first project idea to work towards
Time Commitment & Expectations
Understanding the time investment required will help you plan effectively:
Beginner to Competent (3-6 months)
- 10-15 hours per week of focused study
- Complete Modules 1-3 thoroughly
- Build 2-3 small projects
- Understand core concepts and tools
Competent to Professional (6-12 months)
- 15-20 hours per week including projects
- Complete all modules
- Build a portfolio of 5+ projects
- Contribute to open source or publish articles
Professional to Expert (1-2+ years)
- Continuous learning and specialization
- Real-world project experience
- Research papers and advanced techniques
- Teaching and mentoring others
Ready to Begin Your AI Journey?
Start with the foundations and build your expertise step by step
View AI Foundations → See Complete Roadmap →