🐍 Python – Complete Introduction and Basics for AI/ML

This repository contains a comprehensive guide and examples for learning Python, focusing on its applications in Artificial Intelligence (AI) and Machine Learning (ML). It includes explanations, code snippets, and beginner-friendly examples covering everything from basic syntax to real-world model building using scikit-learn.

📚 Table of Contents

1. What is Python?
2. Why Python for AI/ML?
3. How is Python Different?
4. Applications of Python
5. Arithmetic and Variables
6. Comments & Variables
7. CSV to Python (Pandas)
8. Functions
9. Data Types
10. Conditional Statements
11. Lists
12. Introduction to Machine Learning
13. Building a Model (Decision Tree & Random Forest)
14. Project Flow
15. Requirements
16. How to Run
17. License

🧠 What is Python?

Python is a high-level, interpreted programming language known for its simple and readable syntax.

• Created by Guido van Rossum (1991)
• Dynamically typed – no explicit variable types
• Uses indentation instead of braces {}
• Cross-platform: Works on Windows, Linux, macOS
• Fewer lines of code than C++ or Java

🤖 Why Python for AI/ML?

• Massive library ecosystem: NumPy, Pandas, TensorFlow, PyTorch
• Clean syntax → focus on logic, not boilerplate
• Excellent community support
• Perfect for prototyping and experimentation
• Works seamlessly with Jupyter Notebooks

⚖️ How is Python Different?

Feature	Python	Java/C++
Syntax	Indentation	Braces {}
Typing	Dynamic	Static
Code Length	Shorter	Verbose
Libraries	Huge for AI/ML	Limited
Platform	Cross-Platform	Platform-dependent

🌍 Applications of Python

• AI & Machine Learning → Chatbots, Predictive Models
• Data Science & Analytics → Visualization, Statistics
• Web Development → Django, Flask
• Automation & Scripting → Bots, Testing
• Game Development → Pygame, Panda3D
• IoT & Robotics → Raspberry Pi
• Cybersecurity → Malware Analysis, Scripts

➕ Arithmetic and Variables

print("Hello, world!")
a, b = 5, 3
print(a + b)   # Addition
print(a - b)   # Subtraction
print(a * b)   # Multiplication
print(a / b)   # Division

Use PEMDAS for operation order:
Parentheses → Exponents → Multiplication/Division → Addition/Subtraction

💬 Comments & Variables

# Multiply 3 by 2
print(3 * 2)

test_var = 4 + 5
print(test_var)

🧾 CSV to Python (Pandas)

import pandas as pd
titanic_data = pd.read_csv("train.csv")
titanic_data.head(3)

⚙️ Functions

def add_three(x):
    return x + 3

Example – calculate paint cost:

import math

def get_actual_cost(sqft_walls, sqft_ceiling, sqft_per_gallon, cost_per_gallon):
    total_sqft = sqft_walls + sqft_ceiling
    gallons_needed = total_sqft / sqft_per_gallon
    gallons_to_buy = math.ceil(gallons_needed)
    return cost_per_gallon * gallons_to_buy

🔢 Data Types

Type	Example	Description
int	14	Integer
float	3.14159	Decimal number
bool	True / False	Logical values
str	"Hello"	Text
list	[1,2,3]	Sequence of elements

🔁 Conditional Statements

def evaluate_temp(temp):
    if temp > 38:
        return "Fever!"
    elif temp < 35:
        return "Low temperature."
    else:
        return "Normal temperature."

🌸 Lists

flowers = ["rose", "lily", "orchid"]
print(flowers[0])          # rose
print(flowers[-1])         # orchid
flowers.append("sunflower")
print(len(flowers))        # 4

🤖 Introduction to Machine Learning

Machine Learning (ML) teaches computers to learn from data and improve performance automatically.

Key steps:

1. Load and clean data (pandas)
2. Split into train/test
3. Build model (DecisionTree, RandomForest)
4. Predict
5. Evaluate with Mean Absolute Error (MAE)

🧩 Building a Model (Decision Tree & Random Forest)

import pandas as pd
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv("melb_data.csv")
data = data.dropna(axis=0)

# Target and features
y = data.Price
features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']
X = data[features]

# Split data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# Decision Tree
dt_model = DecisionTreeRegressor(random_state=1)
dt_model.fit(train_X, train_y)
dt_mae = mean_absolute_error(val_y, dt_model.predict(val_X))

# Random Forest
rf_model = RandomForestRegressor(random_state=1, n_estimators=100)
rf_model.fit(train_X, train_y)
rf_mae = mean_absolute_error(val_y, rf_model.predict(val_X))

🔄 Project Flow

CSV → pandas DataFrame → Clean/Preprocess → Split → Train Model → Predict → Evaluate

⚙️ Requirements

pip install pandas scikit-learn numpy

▶️ How to Run

git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>
python filename.py