0.4 Set up your computer (do this now, ~20 minutes)

Step 1 — Install Python

• Mac: brew install python@3.12 (or download from python.org)
• Windows: install from python.org, tick "Add Python to PATH"
• Linux: sudo apt install python3 python3-pip python3-venv

Check it:

python3 --version        # should print 3.10 or higher

Step 2 — Make a project folder with an isolated environment

An environment keeps this project's packages separate from your system, so you can't break anything.

mkdir ai-learning && cd ai-learning
python3 -m venv .venv                 # create the environment
source .venv/bin/activate             # activate it (Mac/Linux)
# Windows:  .venv\Scripts\activate

Your prompt should now start with (.venv). You must run activate every time you open a new terminal.

Step 3 — Install the packages

pip install --upgrade pip
pip install numpy scikit-learn matplotlib jupyter
pip install torch                      # the neural-network library
pip install transformers tokenizers datasets
pip install gensim tiktoken
pip install sentence-transformers faiss-cpu     # for the RAG project

If torch fails or is huge, that's fine — install it when you reach Part 4. For a CPU-only, smaller install: see https://pytorch.org/get-started/locally/ and pick "CPU".

Step 4 — Check everything works

Save this as check_setup.py and run python3 check_setup.py:

import sys
print("python", sys.version.split()[0])

ok = True
for name in ["numpy", "sklearn", "torch", "transformers", "tiktoken"]:
    try:
        m = __import__(name)
        v = getattr(m, "__version__", "?")
        print(f"  OK  {name:15s} {v}")
    except ImportError as e:
        print(f"  MISSING  {name}  ({e})")
        ok = False

if ok:
    import torch
    print("torch sees GPU:", torch.cuda.is_available())      # False is fine
    print("\nSetup looks good. You are ready.")

Step 5 — Learn to use a notebook (optional but recommended)

jupyter notebook

A browser opens. New -> Python 3. Type code in a cell, press Shift+Enter to run it. Notebooks are ideal for experimenting because you can re-run one piece without re-running everything.