Area 00 of 08

Foundations

What AI is and how machines learn from examples, explained from zero. The maths and Python you need, and your first call to an LLM.

Printable cheat sheet for this area

1. 001

   What AI, ML, deep learning and generative AI are

   AI is the big goal of making computers do things that seem smart; machine learning is the main way we get there today, by learning from examples instead of following hand-written rules; deep learning and generative AI are smaller circles inside it.

2. 002

   How a computer learns from examples

   A computer learns by looking at many examples where the answer is known, adjusting itself until its guesses match those answers, and then using what it learned to answer new questions.

   CoversHow a computer learns from examples · Data, features and labels · Training vs inference

3. 003

   Neural networks, starting from one neuron

   A neural network is many tiny calculators (neurons) stacked in layers; each one multiplies its inputs by weights, adds them up and passes the result on, and together they can learn very complicated patterns.

4. 004

   Weights, loss and gradient descent

   Training means finding the weights that make the loss (how wrong the model is) as small as possible, and gradient descent does it by repeatedly taking a small step in whichever direction makes the loss go down.

5. 005

   Overfitting and train, validation and test splits

   Overfitting is when a model memorises its training examples instead of learning the general pattern, so it looks perfect in training and fails on new data; you catch it by keeping some data aside that the model never learns from.

6. 006

   Vectors and matrices, just enough for AI

   A vector is a list of numbers that can describe anything (a word, a photo, a customer), a matrix is a table of numbers, and almost all the maths inside AI is multiplying them together.

7. 007

   Probability, just enough for AI

   An LLM doesn't pick the next word for certain; it gives every possible word a probability, softmax turns its raw scores into those probabilities, and sampling picks one at random following them, which is why the same prompt can give different answers.

8. 008

   Classic machine learning in one lesson

   Before LLMs, most machine learning was (and much still is) three jobs, predicting a number (regression), predicting a category (classification) and finding groups (clustering), done with small, fast models like linear regression and decision trees.

9. 009

   Python for AI engineers

   Almost all AI work happens in Python, so you need a clean setup (a virtual environment per project, installed with uv or pip), notebooks for experiments, and two libraries, numpy for fast maths and pandas for tables.

10. 010

    Calling an LLM API for the first time

    Calling an LLM is just sending an HTTPS request with your API key, a model name and your messages to a provider like OpenAI or Anthropic, and reading the text that comes back, or doing the same for free against a model on your own laptop with Ollama.

11. 011

    Where models come from, and what an AI engineer does

    A few labs (OpenAI, Anthropic, Google, Meta, Mistral and others) train the big models and offer them either through paid APIs or as open weights you can download from hubs like Hugging Face, and an AI engineer's day is choosing between them and building reliable, safe, affordable products on top.

    CoversWhere models come from (OpenAI, Anthropic, Google, Meta, Mistral, Hugging Face) · What an AI engineer does day to day