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Artificial Intelligence Learning Roadmap

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Artificial Intelligence Learning Roadmap (Linux-first)

Want to get into AI but overwhelmed by tutorials, buzzwords, and endless tools? Here’s a straight-to-terminal roadmap: a practical, Linux-first path that shows you what to learn, why it matters, and the exact commands to get moving today.

  • The hook: you don’t need a spaceship to start with AI—your Linux shell is enough.

  • The value: learn to build real, reproducible AI projects using standard tools you already trust (bash, git, Python).

  • The goal: go from zero to runnable models with a clean environment you can maintain.

Why this roadmap works

  • Linux is the default OS for ML research and production. Knowing your way around the terminal is a superpower for AI work.

  • Reproducibility matters. Virtual environments, version control, and deterministic builds make your results credible.

  • Iteration speed beats theory overload. You’ll touch real datasets, run real code, and learn by shipping small, working pieces.

Roadmap at a glance

  1. Prepare your Linux box for AI work
  2. Learn Python + data tooling basics
  3. Start with classical ML (scikit-learn)
  4. Move to deep learning (PyTorch or TensorFlow)
  5. Make it reproducible (git, venv, Makefile, requirements)

Each step includes commands you can paste into your terminal.

1) Prepare your Linux box

Install system packages you’ll use constantly: Python, build tools, git, and common CLI utilities.

  • Debian/Ubuntu (apt):
sudo apt update
sudo apt install -y python3 python3-venv python3-pip git build-essential curl wget jq
  • Fedora/RHEL/CentOS (dnf):
sudo dnf install -y python3 python3-pip git gcc gcc-c++ make curl wget jq
  • openSUSE (zypper):
sudo zypper refresh
sudo zypper install -y python3 python3-pip git gcc gcc-c++ make curl wget jq

Quick sanity check:

python3 --version
pip3 --version
git --version
gcc --version

Create a clean Python virtual environment per project:

mkdir -p ~/ai-roadmap/hello-ai
cd ~/ai-roadmap/hello-ai
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip wheel setuptools

Deactivate later with:

deactivate

2) Python + data basics that actually ship

Install core scientific Python packages and JupyterLab for notebooks:

source .venv/bin/activate
pip install numpy pandas matplotlib jupyterlab

Start JupyterLab:

jupyter lab

Test your stack in a Python REPL or notebook:

python - << 'PY'
import numpy as np, pandas as pd
x = np.random.randn(5, 3)
df = pd.DataFrame(x, columns=list("abc"))
print(df.describe())
PY

Tip: You can also wrangle data in shell. Example: peek a JSON API and pretty-print with jq:

curl -s https://api.github.com/repos/pytorch/pytorch | jq '{name, stargazers_count, language}'

3) Classical ML first: scikit-learn

Scikit-learn gives you a solid foundation in data prep, metrics, and model lifecycles without the overhead of deep learning.

Install:

source .venv/bin/activate
pip install scikit-learn joblib

Minimal, reproducible example (Iris classification):

python - << 'PY'
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score
import joblib

X, y = load_iris(return_X_y=True, as_frame=False)
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000, random_state=42))
pipe.fit(Xtr, ytr)
pred = pipe.predict(Xte)
print("Accuracy:", accuracy_score(yte, pred))

joblib.dump(pipe, "iris_logreg.joblib")
print("Saved model -> iris_logreg.joblib")
PY

Run it:

python iris.py  # or paste the snippet above directly

This teaches core skills you’ll reuse in deep learning: splits, pipelines, metrics, and saving models.

4) Deep learning: choose PyTorch or TensorFlow

You only need one to start. PyTorch is popular in research; TensorFlow/Keras is common in production and mobile. CPU-only is fine while you learn.

  • PyTorch (CPU-only):
source .venv/bin/activate
pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
  • TensorFlow (CPU-only):
source .venv/bin/activate
pip install --upgrade pip
pip install "tensorflow>=2.15"

Verify install and hardware detection:

PyTorch:

python - << 'PY'
import torch
print("PyTorch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
PY

TensorFlow:

python - << 'PY'
import tensorflow as tf
print("TensorFlow:", tf.__version__)
print("Num GPUs Available:", len(tf.config.list_physical_devices('GPU')))
PY

Note on GPUs: Using NVIDIA GPUs requires correct drivers and CUDA/cuDNN versions. Installation differs by distro and GPU model. For reliable instructions, follow the official guides:

  • PyTorch: https://pytorch.org/get-started/locally/

  • TensorFlow: https://www.tensorflow.org/install/pip

Start tiny: a single hidden-layer network on random data (PyTorch example):

python - << 'PY'
import torch, torch.nn as nn, torch.optim as optim

torch.manual_seed(42)
X = torch.randn(512, 10)
y = (X.sum(dim=1) > 0).long()

model = nn.Sequential(nn.Linear(10, 32), nn.ReLU(), nn.Linear(32, 2))
opt = optim.Adam(model.parameters(), lr=1e-2)
loss_fn = nn.CrossEntropyLoss()

for epoch in range(50):
    opt.zero_grad()
    logits = model(X)
    loss = loss_fn(logits, y)
    loss.backward()
    opt.step()
print("Final loss:", float(loss))
PY

5) Reproducibility: glue it together with git, venv, and a Makefile

Version control your code and data dependencies. Keep runs deterministic where possible.

Initialize git:

git init
git config user.name "Your Name"
git config user.email "you@example.com"
echo ".venv/" >> .gitignore
echo "__pycache__/" >> .gitignore

Record your Python environment:

pip freeze > requirements.txt

Recreate later on any Linux box:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Automate routine tasks with a simple Makefile:

cat > Makefile << 'MK'
VENV=.venv
PY=$(VENV)/bin/python
PIP=$(VENV)/bin/pip

.PHONY: venv install train eval clean

venv:
    python3 -m venv $(VENV)
    $(PIP) install --upgrade pip wheel setuptools

install: venv
    $(PIP) install -r requirements.txt

train:
    $(PY) train.py

eval:
    $(PY) eval.py --model artifacts/model.joblib

clean:
    rm -rf __pycache__ .pytest_cache artifacts
MK

Run:

make venv
make install
make train

Optional: manage datasets with DVC (Data Version Control):

source .venv/bin/activate
pip install dvc
dvc init
mkdir -p data
# put raw files in data/, then:
dvc add data/
git add data.dvc .gitignore
git commit -m "Track data with DVC"

Real-world mini-project ideas

  • CLI sentiment analyzer: fine-tune a small model or use classical ML on movie reviews. Add a shell wrapper: echo "I love Linux!" | python predict.py

  • Tabular churn prediction: clean a CSV with pandas, train with scikit-learn, export metrics to a JSON file, and plot with matplotlib.

  • Tiny image classifier: start with CIFAR-10 using torchvision; log training curves and save checkpoints.

Each project should include:

  • A README with “how to run”

  • A requirements.txt

  • A Makefile target for train/eval

  • A seed set for reproducibility (e.g., random_state=42, torch.manual_seed(42))

Troubleshooting quick hits

  • pip errors about build tools: ensure gcc/g++/make are installed (see apt/dnf/zypper commands above).

  • venv not found on Debian/Ubuntu: ensure python3-venv is installed.

  • Jupyter can’t see your venv: start Jupyter from within the activated venv, or install the kernel:

pip install ipykernel
python -m ipykernel install --user --name hello-ai --display-name "Python (hello-ai)"

Conclusion and CTA

You don’t need a cluster or a GPU to start learning AI—you need a clean Linux setup, a few dependable tools, and tight feedback loops.

Your next steps: 1) Create a project folder and venv. 2) Install numpy/pandas/matplotlib + JupyterLab. 3) Ship one classical ML model with scikit-learn. 4) Try a tiny PyTorch or TensorFlow example. 5) Capture your environment with requirements.txt and a Makefile.

Open your terminal and run the first block now. If you get stuck, leave your command and error in a comment—you’ll learn faster by debugging in the open.

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