AI Under the Hood (Beta)

Appearance

TIP

This guide is written with beginners in mind. The examples use Python, the most widely adopted language for AI/ML, thanks to its simplicity and rich ecosystem of libraries.

🔹 Our primary track is Python, to help learners quickly get started with proven tools and libraries.
🔹 A parallel Rust track will be added in the future, focusing on performance and systems-level ML development.

Overview of AI/ML ​

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, enabling computers to perform tasks that usually require human intelligence. This section introduces the core concepts of AI and ML, laying the foundation for your journey into building intelligent systems.

What is AI? ​

AI is a broad field of computer science focused on building systems that can reason, solve problems, understand language, and perceive the world. Everyday examples include:

  • Voice assistants (e.g., Alexa, Siri)
  • Autonomous vehicles
  • Fraud detection systems
  • Game-playing agents like AlphaGo

Key subfields of AI include machine learning, natural language processing (NLP), computer vision, and reinforcement learning.

What is ML? ​

Machine Learning is a subset of AI that enables systems to learn patterns from data and improve performance over time without being explicitly programmed. ML powers applications like:

  • Predicting house prices
  • Recommending movies or products
  • Classifying images (e.g., cat vs. dog)

Types of ML ​

  • Supervised Learning: Learn from labeled data.
    Examples: Linear regression, logistic regression, decision trees.
  • Unsupervised Learning: Discover structure in unlabeled data.
    Examples: Clustering, dimensionality reduction (PCA).
  • Reinforcement Learning: Learn by interacting with an environment to maximize rewards.
    Examples: Training robots, game-playing agents.

Mathematics Behind ML ​

Machine learning is built on mathematical foundations. You don't need to be a math expert to start, but familiarity helps as you go deeper:

  • Linear Algebra: Vectors and matrices for representing data.
  • Calculus: Gradients for optimization (used in training models).
  • Probability & Statistics: Understanding uncertainty and evaluation.

We'll explore these in more detail in the Mathematical Foundations section.

Why Python First? ​

Python is the de facto language of AI/ML because:

  • It has powerful libraries like numpy, pandas, scikit-learn, tensorflow, and pytorch.
  • Its simple syntax makes complex ideas easy to prototype and share.
  • It has an active community and abundant tutorials, datasets, and prebuilt models.

This tutorial blends theory, math, and practical Python code so you can build a strong foundation in AI/ML.

And Why Rust Later? ​

While Python is our starting point, Rust brings unique advantages:

  • Performance and memory safety for large-scale ML workloads.
  • Growing ecosystem with libraries like linfa, tch-rs, and polars.
  • Closer-to-the-metal control for specialized applications.

Once you're comfortable with the basics in Python, exploring Rust will show how the same ideas can be implemented in a high-performance, systems-level environment.

Next Steps ​

Explore Why Python for AI/ML to see why Python dominates the AI ecosystem, or check Tools for the software stack we'll use in this tutorial. Later, you'll also find a Why Rust for AI/ML section to compare the two approaches.

Further Reading ​

  • An Introduction to Statistical Learning by James et al. (Chapter 1)
  • Andrew Ng's Machine Learning Specialization (Course 1, Week 1)
  • The Hundred-Page Machine Learning Book by Andriy Burkov (Chapter 1)