Python Programming Unit 8

AI/ML Readiness

Prepare for machine learning with clean data, evaluation habits, and responsible AI thinking.

This unit prepares students for introductory AI and machine learning. Students learn the scikit-learn workflow, train/test split, preprocessing, fitting, prediction, evaluation, classification, regression, clustering, model metrics, overfitting, baseline models, introductory SciPy and statsmodels concepts when appropriate, responsible AI habits, bias, privacy, data quality, and how to explain model limitations.

Who This Unit Is For

Best for students who have Python, pandas, and visualization experience and want to begin machine learning projects without skipping the foundations.

Learning Goals

Explain the basic machine learning workflow in student-friendly language.
Split data into training and test sets and understand why that matters.
Build simple classification, regression, or clustering examples.
Evaluate a model with appropriate metrics and a baseline comparison.
Discuss bias, privacy, data quality, and model limitations clearly.

Key Concepts

What students practice in this unit

AI projects are most valuable when students understand what the model is doing, how it was tested, and where it can fail. This unit keeps the focus on reasoning, evidence, and responsible communication instead of treating AI as magic.

Train/test workflow

Students learn why a model should be evaluated on examples it did not learn from.

Features and labels

Students identify which columns are inputs, which column is the target, and when a target does not exist.

Evaluation

Students compare accuracy, error, confusion, and baseline results at a beginner-friendly level.

Responsible AI

Students discuss whether the data is fair, private, complete, and appropriate for the prediction being attempted.

Practice

Exercises and mini-project ideas

These are public practice prompts students can use to strengthen the unit without exposing the full internal lesson sequence.

Practice Exercises

Build a simple classifier with a small fictional dataset.
Create a regression prediction and compare predicted values with actual values.
Try a clustering example and describe the groups in plain language.
Practice train/test split and explain why the test set stays separate.
Write an explanation of precision, recall, or error using a simple example.
Complete a responsible AI reflection for a student project idea.

Mini-Project Ideas

Study-hours performance prediction using a small fictional dataset.
Review sentiment classifier using safe sample sentences.
Clustering project that groups anonymous activity summaries.

Short Example

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X = [[2, 1], [3, 2], [8, 7], [9, 8]]
y = ["low", "low", "high", "high"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1)
model = DecisionTreeClassifier(random_state=1)
model.fit(X_train, y_train)
print(model.predict(X_test))

Common Student Mistakes

Training and testing on the same data, then trusting an inflated score.
Using a model before checking missing values or inconsistent categories.
Choosing accuracy when the dataset is highly imbalanced.
Ignoring a simple baseline model.
Presenting predictions as guaranteed facts instead of estimates with limits.

Challenge Extension

Students create a model limitation checklist that covers data source, missing values, possible bias, evaluation metric, and what the model should not be used for.

How This Prepares the Next Step

Students are ready for a larger AI/ML course, portfolio project, or supervised data science mentorship path.

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