Introduction to AI, ML, and DL – With a Dummy Project

🤖 From Basics to Building a Smart Assistant

Introduction
AI, ML, and DL are often used interchangeably, but they are not the same. Let’s break it down and then build something simple.

Breaking It Down

• Artificial Intelligence (AI): The broad idea of machines performing tasks that mimic human intelligence.
• Machine Learning (ML): Subset of AI; systems that learn patterns from data.
• Deep Learning (DL): Subset of ML; uses neural networks with multiple layers for complex tasks.

Dummy Project: Spam Classifier
Goal: Build a simple email spam classifier.
Steps:

1. Collect a dataset (spam vs ham emails).
2. Clean & preprocess text.
3. Train a Naïve Bayes classifier in Python (using scikit-learn).
4. Test with real inputs.

Code Snippet (shortened):

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

X = ["Free money now!!!", "Meeting at 3pm", "Claim your prize"]
y = [1, 0, 1] # 1=spam, 0=ham

vectorizer = CountVectorizer()
X_vec = vectorizer.fit_transform(X)

model = MultinomialNB()
model.fit(X_vec, y)

print(model.predict(vectorizer.transform(["Win a free iPhone"]))) # Output: [1]

Conclusion
Even a small project demonstrates AI/ML/DL in action. The future lies in scaling this up while securing it against misus