What is Artificial Intelligence?

# Simple AI Example: Teaching a Computer to Recognize Patterns
# Without AI: Hard-coded rules (limited and rigid)
def is_spam_email(email):
    if "free money" in email or "click here" in email:
        return True
    return False
# With AI: Learning from examples (flexible and adaptive)
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
# Training data (emails and their labels)
emails = [
    "Win free money now!",
    "Meeting at 3pm tomorrow",
    "Click here for prizes",
    "Project deadline next week"
]
labels = [1, 0, 1, 0]  # 1 = spam, 0 = not spam
# AI learns patterns from data
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(emails)
model = MultinomialNB()
model.fit(X, labels)
# Now it can classify new emails it has never seen!
new_email = ["Free gift waiting for you"]
prediction = model.predict(vectorizer.transform(new_email))
print("Spam!" if prediction[0] == 1 else "Not spam!")
# The AI learned patterns without explicit rules!

# Simple AI Workflow Example
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 1. COLLECT DATA
# Example: Predicting if a student will pass based on study hours
study_hours = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).reshape(-1, 1)
passed = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])  # 0=fail, 1=pass
# 2. TRAIN MODEL
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    study_hours, passed, test_size=0.3, random_state=42
)
# Create and train the AI model
model = LogisticRegression()
model.fit(X_train, y_train)
# 3. TEST & VALIDATE
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
print(f"Model Accuracy: {accuracy * 100}%")
# 4. USE THE MODEL (Make predictions on new data)
new_student_hours = np.array([[5.5]])
prediction = model.predict(new_student_hours)
print(f"Student studying 5.5 hours will: {'PASS' if prediction[0] == 1 else 'FAIL'}")
# The AI learned the pattern: more study hours → higher chance of passing!