15 🧠 Machine Learning and AI

Machine Learning (ML) and Artificial Intelligence (AI) enable computers to learn from data and make predictions. Python is the leading language for ML & AI due to its rich ecosystem of libraries.

This chapter covers scikit-learn, TensorFlow, and PyTorch, which are widely used for data preprocessing, building ML models, and deep learning applications.

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15.1 🔍 What is Machine Learning?

Machine Learning is a subset of AI that focuses on teaching computers to learn from data. It is classified into:

Type	Description	Example
Supervised Learning	Uses labeled data	Spam detection, house price prediction
Unsupervised Learning	Finds hidden patterns	Clustering customers, anomaly detection
Reinforcement Learning	Learns by trial and error	Game AI, robotics

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15.2 🛠️ Scikit-Learn – Machine Learning for Beginners

Scikit-learn is a simple and powerful ML library for classification, regression, clustering, and preprocessing.

✅ Installing Scikit-Learn

pip install scikit-learn

🔹 Loading a Dataset

from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
print(df.head())

✅ Use Case: Loading datasets for training ML models.

🔹 Training a Simple Classifier

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load dataset
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

# Make predictions
y_pred = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

✅ Use Case: Building predictive models for classification problems.

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15.3 🤖 TensorFlow – Deep Learning Framework

TensorFlow is a powerful framework for building deep learning models.

✅ Installing TensorFlow

pip install tensorflow

🔹 Creating a Simple Neural Network

import tensorflow as tf
from tensorflow import keras

# Define a simple model
model = keras.Sequential([
    keras.layers.Dense(16, activation='relu', input_shape=(4,)),
    keras.layers.Dense(3, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=8)

✅ Use Case: Training neural networks for AI applications.

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15.4 🔥 PyTorch – Deep Learning for Research & AI

PyTorch is widely used in AI research and deep learning experiments.

✅ Installing PyTorch

pip install torch torchvision

🔹 Creating a Simple Neural Network

import torch
import torch.nn as nn
import torch.optim as optim

# Define the model
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(4, 16)
        self.fc2 = nn.Linear(16, 3)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.softmax(self.fc2(x), dim=1)

model = SimpleNN()

✅ Use Case: Developing custom AI models for image recognition, NLP, and robotics.

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15.5 📊 Model Evaluation and Metrics

Measuring model performance is critical in ML & AI.

Metric	Use Case
Accuracy	Classification problems
Precision & Recall	Imbalanced datasets (e.g., fraud detection)
Mean Squared Error (MSE)	Regression models
ROC Curve	Evaluating classification models

🔹 Evaluating a Model

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

✅ Use Case: Assessing model performance before deployment.

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🚀 Summary

Library	Purpose	Best For
Scikit-learn	Traditional ML models	Classification, regression, clustering
TensorFlow	Deep learning	Neural networks, AI applications
PyTorch	AI research	Custom AI models, NLP, computer vision

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🔚 Final Thoughts

Machine Learning and AI enable automation, predictions, and decision-making in various industries.

Would you like hands-on projects on ML & AI? 🚀