#The hook

In 2024, an AI program won the Nobel Prize in Chemistry. The language it was written in is the same one a 12-year-old uses for their first "guess the number" project on day one of an after-school class.

# Your first taste of Python — running in your browser, right now
name = "future AI engineer"
years = 2026
print(f"Hello, {name}!")
print(f"Welcome to the year {years}.")
print(f"You are about to learn the language that's eating the world.")

#Why this matters in 2026 — the receipts

Numbers first. Python's grip on the AI/ML world is not a feeling — it's a measurable hegemony.

PyTorch is Python. TensorFlow is Python. JAX is Python. Hugging Face Transformers is Python. The Anthropic SDK and OpenAI SDK are Python. ChatGPT's RLHF training code is Python. Every fine-tuning notebook on the planet is Python. If you can read this, you have already met 60% of what you need to participate.

“Python is the second-best language for everything. Which is exactly why it wins.”

#The 30-second answer — what makes Python special

Strip it down. Python does four things uniquely well, and stacking them is what makes it the AI/ML default. Every other language does one or two; Python does all four.

#How it actually feels — the tour

#1. Variables and types — no ceremony

# Python figures out the type from the value. No type declaration needed.
name = "Ada"          # a string
age = 31              # an integer
height = 5.6          # a float
is_pioneer = True     # a boolean
favourite_numbers = [42, 137, 1729]  # a list

print(f"{name} is {age} years old, {height}ft tall, pioneer = {is_pioneer}.")
print(f"Lucky numbers: {favourite_numbers}, biggest = {max(favourite_numbers)}.")

#2. Loops and conditions — readable as prose

# Filter a list of grades, label each one. Reads top to bottom in English.
grades = [88, 42, 91, 67, 73, 55, 95]

for g in grades:
    if g >= 90:
        label = "A"
    elif g >= 75:
        label = "B"
    elif g >= 60:
        label = "C"
    else:
        label = "needs work"
    print(f"  {g} -> {label}")

#3. Functions — small, named, reusable

# A function is a named block of code you can call by its name.
def is_prime(n):
    """Return True if n is a prime number."""
    if n < 2:
        return False
    for divisor in range(2, int(n ** 0.5) + 1):
        if n % divisor == 0:
            return False
    return True

# Try it
for x in range(2, 20):
    if is_prime(x):
        print(f"{x} is prime")

#4. List comprehensions — the Python superpower

# Build a list inline. Reads like a math expression.
squares = [n ** 2 for n in range(1, 11)]
evens = [n for n in range(20) if n % 2 == 0]
uppercased = [w.upper() for w in ["hello", "world", "python"]]

print("Squares:    ", squares)
print("Evens:      ", evens)
print("Uppercased: ", uppercased)

A list comprehension does in one line what most languages need a for-loop, an if-block, and an .append() to do. This pattern shows up everywhere in numerical Python.

#5. The 5-line program that calls a real ML model

This is where Python earns its place in AI. Here's a real machine-learning model — a random forest — trained on real data, predicting a real flower species, in 6 lines:

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

X, y = load_iris(return_X_y=True)
model = RandomForestClassifier(n_estimators=100, random_state=42).fit(X, y)
species = ["setosa", "versicolor", "virginica"]

new_flower = [[5.1, 3.5, 1.4, 0.2]]
prediction = model.predict(new_flower)[0]
print(f"Predicted species: {species[prediction]}")
print(f"Confidence:        {model.predict_proba(new_flower)[0][prediction]:.0%}")

This is what people mean when they say Python is "the language AI spoke first." That same six-line pattern, scaled up to billions of parameters and trained on the internet, is GPT-4.

#The 2026 frontier — Python is finally fast

• PEP 703 — Free-threading (3.13 experimental, 3.14 stable in 2026): the GIL is now optional. Multi-core Python without multiprocessing workarounds.
• PEP 744 — JIT compiler (3.13+): a copy-and-patch JIT layer that makes hot loops 10–60% faster with no code changes.
• uv (the new pip + venv replacement, written in Rust): 10–100× faster package installs. The Python ecosystem is finally getting the tooling it deserved a decade ago.

Combined: a 2026 Python program can saturate 32 cores, JIT-compile its hot paths, and install its dependencies in 4 seconds instead of 4 minutes. The "Python is slow" critique is rapidly dating itself.

#What's been built with Python

Six systems you've used today, and the Python that runs them:

#Run a real ML benchmark — interactive

To make Python's "glue language" superpower concrete: the cell below benchmarks a real numerical operation (a 1000×1000 matrix multiply) — first in pure Python, then via NumPy, which calls into 30-year-old optimized BLAS Fortran. Watch the speedup.

import time
import numpy as np

# Build two 200x200 random matrices (kept small so pure-Python finishes fast)
n = 200
A = [[(i * j) % 7 for j in range(n)] for i in range(n)]
B = [[(i + j) % 5 for j in range(n)] for i in range(n)]

# Pure-Python matrix multiply — three nested loops
start = time.time()
C = [[0] * n for _ in range(n)]
for i in range(n):
    for j in range(n):
        for k in range(n):
            C[i][j] += A[i][k] * B[k][j]
pure_time = time.time() - start
print(f"Pure Python:   {pure_time*1000:7.1f} ms")

# Same operation via NumPy — single line, calls into BLAS under the hood
A_np, B_np = np.array(A), np.array(B)
start = time.time()
C_np = A_np @ B_np
numpy_time = time.time() - start
print(f"NumPy:         {numpy_time*1000:7.1f} ms")
print(f"Speedup:       {pure_time / numpy_time:.0f}x faster")

#Where to go next

If this clicked, here's the path through the curriculum that takes you from "I just ran my first Python" to "I'm building real AI tools":

1. Python Foundations track — 39 lessons, beginner to fluent. Variables → loops → functions → OOP → file I/O → APIs → async. Every code block runs in your browser.
2. Math Foundations — 17 lessons covering vectors, calculus, probability — the math you actually need for ML. Read the Math Foundations blog (coming next) for the high-level overview first.
3. Data Foundations — 15 lessons on real-world data pipelines: cleaning, feature engineering, train/test split, drift detection. The 80% of ML work that decides whether your model succeeds.
4. Classical ML — your first real models. Linear regression to XGBoost. The 80% of "AI" that isn't an LLM.

By the end of those four tracks, you'll have written hundreds of lines of working Python and trained a dozen models — without ever installing anything locally.

#Key takeaways

#References & further reading

• The Python Tutorial (docs.python.org/3/tutorial/) — the official walkthrough. Better than 90% of paid courses.
• Mark Lutz — Learning Python (5th ed). The definitive doorstop. Skip the first 100 pages if you're impatient.
• Luciano Ramalho — Fluent Python (2nd ed, 2022). What you read once you're past beginner.

• Python for Data Analysis by Wes McKinney (the creator of Pandas). 3rd edition, 2022.
• Sebastian Raschka — Machine Learning with PyTorch and Scikit-Learn (2022). Best on-ramp from Python to ML.
• Jeremy Howard — fast.ai courses. Python + PyTorch from the top down. Free.

• PEP 703 — Making the GIL Optional in CPython. (peps.python.org/pep-0703)
• PEP 744 — JIT Compilation. The copy-and-patch JIT shipping in 3.13+.
• Astral.sh / uv — the Rust-written package installer that's 10–100× faster than pip. The new default for serious work.