Appendix A: Python and Colab Primer

Appendix A: Python and Colab Primer#

Read this before Unit 3 if you have never run code before. It takes about ten minutes and requires no installation. You will not be asked to write programs in this course — but you will run them, change small pieces, and interpret what comes out.

Part I — Getting Started with Google Colab#

Google Colab runs Python in your browser. Nothing to install, works on any laptop, free with a Google account.

Step 1 — Open a notebook#

Go to colab.research.google.com and choose New notebook. Or click the 🚀 rocket icon at the top of any page in this book.

Step 2 — Save your own copy#

Warning

Do this first, every time. Click File → Save a copy in Drive. If you skip this, you are working in a temporary copy and everything you do disappears when you close the tab. This is the single most common way students lose their work.

Step 3 — Run a cell#

Click into a code cell and press Shift + Enter (or the ▶ play button). Try this:

print("Hello from Colab")
2 + 2

The first run takes ten to twenty seconds while Colab starts a machine for you. Subsequent runs are instant.

Step 4 — Know your shortcuts#

Action	Shortcut
Run cell, move to next	Shift + Enter
Run cell, stay put	Ctrl + Enter
Add cell below	Ctrl + M then B
Delete cell	Ctrl + M then D
Interrupt a stuck cell	Runtime → Interrupt execution
Start completely fresh	Runtime → Restart session

Part II — Just Enough Python#

You need to read these six things, not write them from memory.

Variables — names for values#

course = "Foundations of AI"      # text (a "string")
credits = 3                        # whole number (an "integer")
gpa = 3.75                         # decimal ("float")
enrolled = True                    # yes/no ("boolean")

print(course, credits, gpa, enrolled)

Lists — ordered collections#

units = ["History", "Foundations", "How AI Works", "ML Paradigms"]

print(units[0])       # 'History'  -- counting starts at ZERO
print(units[-1])      # 'ML Paradigms'  -- negative counts from the end
print(len(units))     # 4

Note

Counting from zero trips up everyone at first. The first item is [0], the second is [1]. Whenever code surprises you by being “off by one,” this is usually why.

Loops — do something repeatedly#

for unit in units:
    print(f"This week: {unit}")

The indentation matters in Python. The indented line is what repeats. This is not a style preference — it is the syntax.

Functions — reusable operations#

def letter_grade(score):
    if score >= 93: return "A"
    elif score >= 90: return "A-"
    elif score >= 87: return "B+"
    elif score >= 83: return "B"
    else: return "see the syllabus"

print(letter_grade(94))
print(letter_grade(85))

Libraries — borrowing other people’s work#

Almost all real code imports tools rather than building from scratch:

import numpy as np              # numerical arrays
import pandas as pd             # tables of data
import matplotlib.pyplot as plt # charts

The as np part creates a short nickname. When you see np.mean(...), it means “the mean function from numpy.”

Comments — notes for humans#

# Anything after a hash mark is ignored by Python.
# The code snippets in this book use comments heavily
# to explain what each section is doing. Read them.

Part III — Reading Error Messages#

Errors are normal and are not a sign you did something stupid. Read the last line first — it names the problem.

Error	Usual meaning	Fix
`NameError: name 'x' is not defined`	Used something before creating it	Run the earlier cell first
`ModuleNotFoundError`	Library not installed	Add `!pip install <name>`
`IndentationError`	Spacing is inconsistent	Align the indented block
`SyntaxError`	Typo — often a missing `)` or `:`	Check the line above too
`KeyError`	Column name doesn’t exist	Check spelling and capitalization
`ValueError: shapes not aligned`	Data dimensions mismatch	Print `.shape` to inspect

Important

The most common problem in Colab is running cells out of order. Cells share memory, so a cell that worked yesterday may fail today because you skipped the one that defined its variables. When things get strange: Runtime → Restart session, then run every cell from the top.

Using AI to debug#

Per this course’s AI policy, using an AI assistant on lab activities is encouraged. A productive approach:

I’m getting this error in Python. Explain what it means in plain language and what I should check. Don’t just give me corrected code — I want to understand it.

[paste the full error message and the code that caused it]

Asking for an explanation rather than a fix is the difference between learning and copying. Your exams do not permit AI (see Appendix C).

Part IV — What This Course Uses#

Every snippet in this book runs on the stock Colab environment. These are already installed:

Library	Purpose	Units
`numpy`	Numerical arrays and math	Throughout
`pandas`	Data tables	6, 8, 9, 10, 12
`matplotlib`	Charts and plots	Throughout
`scikit-learn`	Machine learning models	3, 4, 5, 6, 9, 10
`scipy`	Scientific computing	13

Only Unit 7 requires an install (transformers), and the snippet includes the command.

Part V — Verify Your Setup#

Run this before Unit 3. If it prints without error, you are ready for the whole course.

import sys
import numpy as np, pandas as pd, matplotlib, sklearn, scipy
import matplotlib.pyplot as plt

print(f"Python       {sys.version.split()[0]}")
print(f"numpy        {np.__version__}")
print(f"pandas       {pd.__version__}")
print(f"matplotlib   {matplotlib.__version__}")
print(f"scikit-learn {sklearn.__version__}")
print(f"scipy        {scipy.__version__}")

# A quick end-to-end check: train a model and draw a chart
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, random_state=0)
acc = DecisionTreeClassifier(random_state=0).fit(X_tr, y_tr).score(X_te, y_te)

plt.figure(figsize=(5, 3))
plt.bar(["accuracy"], [acc], color="seagreen")
plt.ylim(0, 1); plt.title(f"Setup OK — model accuracy {acc:.0%}")
plt.tight_layout(); plt.show()

print("\n✅ Everything works. You are ready for Unit 3.")

Alternatives to Colab#

You are welcome to use any Python environment you prefer:

Option	Notes
Google Colab	Recommended. Nothing to install.
Anaconda / Jupyter	Local install; good if you want offline work
VS Code + Python extension	Popular with developers
Kaggle Notebooks	Similar to Colab, also free
PyCharm	Full IDE; more setup

Every snippet in this book is plain Python and runs anywhere. The only Colab-specific line in the entire book is the !pip install in Unit 7 — outside Colab, run pip install transformers in your terminal instead.