Chapter 3: Python for AI 🔧 Python is the most popular programming language for Artificial Intelligence due to its simplicity, flexibility, and powerful libraries. 🐍 1. Python Basics for AI Before diving into AI libraries, you must understand basic Python programming. 🔹 Key Concepts: Concept Example Use in AI Variables x = 10 Store data Data Types int, float, str, bool, list, dict Organize data Conditions if, elif, else Decision-making Loops for, while Repeating tasks Functions def greet(): Reusable blocks of code ✅ Mini Practice: python Copy code def greet(name): return "Hello, " + name print(greet("AI Learner")) 📚 2. Essential Libraries for AI These libraries make AI development easy and efficient. 🧮 NumPy (Numerical Python) Used for handling arrays, matrices, and mathematical operations. python Copy code import numpy as np arr = np.array([1, 2, 3]) print(arr.mean()) 🔸 Use in AI: Linear Algebra, Data Transformation, Activation Functions 📊 Pandas (Data Analysis Library) Helps in data manipulation, cleaning, and analysis. python Copy code import pandas as pd df = pd.read_csv("data.csv") print(df.head()) 🔸 Use in AI: Preprocessing, feature selection, data visualization 📈 Matplotlib (Data Visualization) Used to plot charts like line graphs, bar charts, histograms. python Copy code import matplotlib.pyplot as plt x = [1, 2, 3] y = [2, 4, 6] plt.plot(x, y) plt.show() 🔸 Use in AI: Visualizing data and training progress 🧪 3. Hands-on: Load a Dataset & Preprocess It 📝 Load Data: python Copy code import pandas as pd df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv") print(df.head()) 🧹 Basic Preprocessing: python Copy code print(df.isnull().sum()) # Check for missing values df['species'] = df['species'].astype('category').cat.codes # Encode labels 🔁 Common Preprocessing Steps in AI: Step Tool Description Missing values Pandas Fill or drop Scaling sklearn Normalize features Encoding Pandas/LabelEncoder Convert text labels to numbers Splitting sklearn Train-test split 🧠 Summary of Chapter 3: Topic Summary Python Basics Learn syntax, variables, loops, and functions NumPy Math and matrix operations Pandas Handle and clean datasets Matplotlib Visualize trends and model outputs Hands-on Load real data and perform simple preprocessing ✅ Mini Assignment: Write Python code to: Load a CSV file using Pandas Replace missing values with column mean Plot a bar chart of one column Use NumPy to create a 2D matrix and compute its transpose.

 

Chapter 3: Python for AI

🔧 Python is the most popular programming language for Artificial Intelligence due to its simplicity, flexibility, and powerful libraries.

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🐍 1. Python Basics for AI

Before diving into AI libraries, you must understand basic Python programming.

🔹 Key Concepts:

Concept	Example	Use in AI
Variables	x = 10	Store data
Data Types	int, float, str, bool, list, dict	Organize data
Conditions	if, elif, else	Decision-making
Loops	for, while	Repeating tasks
Functions	def greet():	Reusable blocks of code

✅ Mini Practice:

python
def greet(name):
    return "Hello, " + name
print(greet("AI Learner"))

---

📚 2. Essential Libraries for AI

These libraries make AI development easy and efficient.

---

🧮 NumPy (Numerical Python)

Used for handling arrays, matrices, and mathematical operations.

python
import numpy as np
arr = np.array([1, 2, 3])
print(arr.mean())

🔸 Use in AI: Linear Algebra, Data Transformation, Activation Functions

---

📊 Pandas (Data Analysis Library)

Helps in data manipulation, cleaning, and analysis.

python
import pandas as pd
df = pd.read_csv("data.csv")
print(df.head())

🔸 Use in AI: Preprocessing, feature selection, data visualization

---

📈 Matplotlib (Data Visualization)

Used to plot charts like line graphs, bar charts, histograms.

python
import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [2, 4, 6]
plt.plot(x, y)
plt.show()

🔸 Use in AI: Visualizing data and training progress

---

🧪 3. Hands-on: Load a Dataset & Preprocess It

📝 Load Data:

python
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv")
print(df.head())

🧹 Basic Preprocessing:

python
print(df.isnull().sum())  # Check for missing values
df['species'] = df['species'].astype('category').cat.codes  # Encode labels

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🔁 Common Preprocessing Steps in AI:

Step	Tool	Description
Missing values	Pandas	Fill or drop
Scaling	sklearn	Normalize features
Encoding	Pandas/LabelEncoder	Convert text labels to numbers
Splitting	sklearn	Train-test split

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🧠 Summary of Chapter 3:

Topic	Summary
Python Basics	Learn syntax, variables, loops, and functions
NumPy	Math and matrix operations
Pandas	Handle and clean datasets
Matplotlib	Visualize trends and model outputs
Hands-on	Load real data and perform simple preprocessing

---

✅ Mini Assignment:

1. Write Python code to:

   • Load a CSV file using Pandas

   • Replace missing values with column mean

   • Plot a bar chart of one column

2. Use NumPy to create a 2D matrix and compute its transpose.