MODULE 16: Python for AI & Machine Learning

Python is the most popular language for AI & Machine Learning due to its simplicity, strong libraries, and large community support.

1. What is AI & ML?

Artificial Intelligence (AI)

AI refers to systems that can simulate human intelligence, such as:

• Learning from data
• Making decisions
• Recognizing patterns
• Understanding language

Examples

• Chatbots
• Face recognition
• Recommendation systems

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Machine Learning (ML)

ML is a subset of AI where systems learn from data without being explicitly programmed.

Real-World Examples

• Spam email detection
• Job recommendation engines
• Salary prediction
• Resume screening (ATS)

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AI vs ML vs DL

Concept	Meaning
AI	Broad concept of intelligent machines
ML	Learning from data
DL	Neural networks with multiple layers

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2. Scikit-Learn Basics

Brief

Scikit-learn is the most widely used ML library in Python.

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Key Features

• Simple and consistent API
• Built-in datasets
• Multiple ML algorithms
• Model evaluation tools

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Install Scikit-Learn

pip install scikit-learn

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Basic Imports

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

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3. Supervised Learning

Brief

Supervised learning uses labeled data (input + output).

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Types

• Regression → Predict continuous values
• Classification → Predict categories

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Example Use Cases

• Salary prediction
• Email spam detection
• Resume shortlisted or not

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Simple Example (Classification)

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

data = load_iris()
X = data.data
y = data.target

model = DecisionTreeClassifier()
model.fit(X, y)

print(model.predict([[5.1, 3.5, 1.4, 0.2]]))

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4. Unsupervised Learning

Brief

Unsupervised learning uses unlabeled data.

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Common Algorithms

• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• PCA (Dimensionality Reduction)

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Example: K-Means Clustering

from sklearn.cluster import KMeans

X = [[1, 2], [1, 4], [1, 0],
     [10, 2], [10, 4], [10, 0]]

model = KMeans(n_clusters=2)
model.fit(X)

print(model.labels_)

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Real-World Use

• Customer segmentation
• Resume grouping
• Market analysis

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5. Model Training

Brief

Model training means teaching the model patterns from data.

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Training Workflow

1. Collect data
2. Clean & preprocess
3. Split data
4. Train model
5. Tune parameters

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Train-Test Split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

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Fit Model

model.fit(X_train, y_train)

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6. Model Evaluation

Brief

Evaluating a model checks how well it performs on unseen data.

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Common Metrics

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

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Accuracy Example

from sklearn.metrics import accuracy_score

y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

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Classification Report

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

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Why Evaluation Matters

• Prevents overfitting
• Ensures real-world performance

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7. Simple ML Projects

Project 1: Iris Flower Classification

• Dataset: Iris
• Algorithm: Decision Tree / KNN
• Output: Flower type

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Project 2: House Price Prediction

• Algorithm: Linear Regression
• Input: Area, rooms, location
• Output: Price

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Project 3: Student Performance Prediction

• Input: Study hours
• Output: Pass / Fail

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Project 4: Resume Shortlisting (Basic)

• Input: Skills count
• Output: Shortlisted or not

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Mini Project Code (Linear Regression)

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4]])
y = np.array([30000, 35000, 40000, 45000])

model = LinearRegression()
model.fit(X, y)

print(model.predict([[5]]))