Tutorial Roadmap

This roadmap outlines the AI/ML tutorial, designed with a dual track approach:

• Python-first: Beginners start here, leveraging Python's simplicity and rich ecosystem.
• Rust (coming soon): Advanced learners can explore Rust's performance and safety advantages.

The structure is organized into 9 modules, ensuring you build both theoretical knowledge and practical coding skills.

Modules Overview

1. Introduction: Understand AI/ML basics, why Python (and later Rust), and the tools used.
2. Getting Started: Set up Python (and optionally Rust), run your first ML model.
3. Mathematical Foundations: Linear algebra, calculus, probability, and statistics.
4. Core Machine Learning: Regression, classification, clustering, and evaluation techniques.
5. Deep Learning: Neural networks, CNNs, RNNs, transformers, and optimization.
6. Practical ML Skills: Data preprocessing, model tuning, experiment tracking, and deployment (FastAPI in Python, Actix in Rust).
7. Advanced Topics: NLP, computer vision, reinforcement learning, ethics, and generative AI.
8. Projects: Real-world case studies like house price prediction, image classification, and text sentiment analysis.
9. Resources: Curated Python and Rust libraries, recommended books, and learning communities.

Learning Path

Start with Introduction and Getting Started to set up your environment.
Then move into Mathematical Foundations before tackling Core ML and Deep Learning.
Enhance your skills with Practical ML Skills and Advanced Topics, and solidify them through Projects.
Finally, dive deeper with Resources tailored to both Python and Rust learners.

Next Steps

• Begin with Setup to install Python (and optionally Rust).
• Review Tools for the libraries we'll use.

Further Reading

• An Introduction to Statistical Learning by James et al. (Chapter 1)
• Andrew Ng's Machine Learning Specialization (Course 1, Week 1)
• Artificial Intelligence: A Modern Approach by Russell and Norvig (Chapter 1)