Python & AI Intro: Learn to Frame Problems Before Chasing Models

What you're probably confused about right now

"AI sounds hard. Do I need a ton of math first?"

At this stage, focus on "input-process-output" modeling. Get the problem framed clearly first.

One-line definition

AI intro is about turning real-world problems into executable workflows, then gradually upgrading from rules to models.

Real-life analogy

Ordering bubble tea: input your order -> rules process it -> output the drink.

Minimal working example

text = "learn python for data analysis"
label = "Data" if "data" in text else "General"
print(label)

Quick quiz (5 min)

1. Expand to 3 text categories.
2. Count hits per category.
3. Output a classification report.

Quiz answer guide & grading criteria

• Answer direction: write runnable code that covers the core requirements and edge cases from the prompt.
• Criterion 1 (Correctness): Main flow produces correct results, key branches execute.
• Criterion 2 (Readability): Clear variable names, no excessive nesting.
• Criterion 3 (Robustness): Basic protection against null values, type errors, or unexpected input.

Take-home task

Build a "learning direction recommender" v1 (rule-based version).

Acceptance criteria

You can independently:

• Describe an AI task using input/process/output
• Write a minimal rule-based classifier
• Explain the difference between a rule-based approach and a model-based approach

Common errors & debugging steps (beginner edition)

• Can't read the error: start from the last line -- find the error type (TypeError, NameError, etc.), then trace back to the line in your code.
• Not sure about a variable's value: throw in a temporary print(var, type(var)) at key points to verify data looks right.
• Changed code but nothing happened: make sure the file is saved, you're running the right file, and your terminal is in the correct venv.

Common misconceptions

• Misconception: jump straight to SOTA models.
• Reality: build problem-modeling skills first, then level up to models.