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‎AIwithPython/README.md‎

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# 🧠 AI with Python
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Welcome to **AI with Python** — a beginner-friendly and practical repository that explores how **Artificial Intelligence (AI)** works and how you can build intelligent systems using **Python**.
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---
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## 🌍 What is Artificial Intelligence (AI)?
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**Artificial Intelligence (AI)** is the broad field of computer science focused on creating systems that can **think, learn, and act like humans**.
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AI enables machines to perform tasks that normally require human intelligence — such as reasoning, understanding language, recognizing patterns, solving problems, and making decisions.
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Examples:
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- Chatbots that understand natural language
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- Recommendation systems (Netflix, Spotify, YouTube)
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- Autonomous vehicles
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- Image recognition and voice assistants
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---
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## 🤖 AI vs ML vs DL vs GenAI
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| Concept | Description | Example |
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|----------|--------------|----------|
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| **AI (Artificial Intelligence)** | The *umbrella field* — aims to create intelligent systems that can simulate human-like thinking and decision-making. | A self-driving car deciding when to stop or go. |
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| **ML (Machine Learning)** | A **subset of AI** focused on teaching machines to learn from data without being explicitly programmed. | A spam filter that improves as it processes more emails. |
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| **DL (Deep Learning)** | A **subset of ML** that uses **neural networks with many layers** to automatically learn complex features from data. | Facial recognition or voice assistants like Siri. |
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| **GenAI (Generative AI)** | A **type of AI model** (often based on DL) that can **generate new content** — text, images, music, or code — similar to what humans create. | ChatGPT, DALL·E, or Stable Diffusion. |
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**Hierarchy Visualization:**
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AI
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├── Machine Learning (ML)
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│ ├── Deep Learning (DL)
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│ │ └── Generative AI (GenAI)
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---
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## 🐍 Why Python for AI?
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Python is the most popular language for AI and ML because it is:
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- Easy to learn and read
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- Supported by a wide ecosystem of powerful libraries:
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- **NumPy**, **Pandas** → Data handling
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- **Matplotlib**, **Seaborn** → Visualization
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- **Scikit-learn** → Machine Learning
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- **TensorFlow**, **PyTorch**, **Keras** → Deep Learning
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- **Transformers**, **LangChain** → Generative AI
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---
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## 📘 Topics Covered
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This repository will guide you through:
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1. **Python Basics for AI** – Variables, data types, loops, functions
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2. **Data Science Foundations** – Numpy, Pandas, and data preprocessing
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3. **Machine Learning** – Supervised and Unsupervised learning
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4. **Deep Learning** – Neural networks and CNNs
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5. **Generative AI** – Transformers, LLMs, and text/image generation
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6. **AI Deployment** – Using AWS SageMaker and APIs
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---
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## 🚀 Getting Started
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‎Advanced_Python/PythonOOP.py‎

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#OOP Python
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'''
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Object Oriented Programming is a fundamental concept in Python,
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empowering developers to build modular, maintainable and scalable applications.
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OOP is a way of organizing code that uses objects and classes to represent
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real-world entities and their behavior. In OOP, object has attributes thing
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that has specific data and can perform certain actions using methods.
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'''
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class Dog:
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species = "Canine" # Class attribute
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def __init__(self, name, age):
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self.name = name # Instance attribute
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self.age = age # Instance attribute
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# Creating an object of the Dog class
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dog1 = Dog("Buddy", 3)
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print(dog1.name)
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print(dog1.species)
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#Encapsulation example
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class Dog:
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def __init__(self, name, breed, age):
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self.name = name # Public attribute
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self._breed = breed # Protected attribute
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self.__age = age # Private attribute
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# Public method
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def get_info(self):
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return f"Name: {self.name}, Breed: {self._breed}, Age: {self.__age}"
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# Getter and Setter for private attribute
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def get_age(self):
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return self.__age
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def set_age(self, age):
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if age > 0:
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self.__age = age
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else:
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print("Invalid age!")
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# Example Usage
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dog = Dog("Buddy", "Labrador", 3)
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# Accessing public member
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print(dog.name) # Accessible
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# Accessing protected member
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print(dog._breed) # Accessible but discouraged outside the class
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# Accessing private member using getter
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print(dog.get_age())
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# Modifying private member using setter
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dog.set_age(5)
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print(dog.get_info())
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#Inheritance example
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class Animal:
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def speak(self):
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return "Animal speaks"
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class Dog(Animal):
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def speak(self):
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return "Woof!"
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class Cat(Animal):
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def speak(self):
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return "Meow!"
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dog = Dog()
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cat = Cat()
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print(dog.speak()) # Output: Woof!
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print(cat.speak()) # Output: Meow!
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# Method Overriding
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# We start with a base class and then a subclass that "overrides" the speak method.
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class Animal:
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def speak(self):
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return "I am an animal."
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class Dog(Animal):
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def speak(self):
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return "Woof!"
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print(Dog().speak())
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# 2 Duck Typing
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class Cat:
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def speak(self):
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return "Meow!"
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def make_animal_speak(animal):
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# This function works for both Dog and Cat because they both have a 'speak' method.
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return animal.speak()
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print(make_animal_speak(Cat()))
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print(make_animal_speak(Dog()))
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# 3 Operator Overloading
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# We create a simple class that customizes the '+' operator.
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class Vector:
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def __init__(self, x, y):
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self.x = x
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self.y = y
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def __add__(self, other):
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# This special method defines the behavior of the '+' operator.
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return Vector(self.x + other.x, self.y + other.y)
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def __repr__(self):
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return f"Vector({self.x}, {self.y})"
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v1 = Vector(2, 3)
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v2 = Vector(4, 5)
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v3 = v1 + v2
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print(v3)

‎Data_Structure/PythonArray.py‎

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#Python Arrays
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import array as arr
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a = arr.array('i', [1, 2, 3])
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# accessing First Araay
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print(a[0])
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# adding element to array
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a.append(5)
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print(a)
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a = [1, "Hello", [3.14, "world"]]
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a.append(2) # Add an integer to the end
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print(a)
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a = arr.array('i', [1, 2, 3, 1, 5])
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# remove first occurance of 1
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a.remove(1)
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print(a)
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# remove item at index 2
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a.pop(2)
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print(a)
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a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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b = arr.array('i', a)
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res = a[3:8]
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print(res)
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res = a[5:]
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print(res)
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res = a[:]
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print(res)
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import numpy as np
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a = np.array([1, 2, 3, 4])
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# Element-wise operations
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print(a * 2)
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# Multi-dimensional array
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res = np.array([[1, 2], [3, 4]])
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print(res * 2)
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import array
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a = array.array('i', [1, 2, 3, 1, 2, 5])
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# update item at index 2
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a[2] = 6
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print(a)
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# update item at index 4
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a[4] = 8
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print(a)
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import array
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a = array.array('i', [1, 2, 3, 4, 5])
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a.reverse()
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print(*a)

‎Data_Structure/PythonList.py‎

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#Python Lists
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a = [1, 2, 3, 4, 5] # List of integers
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b = ['apple', 'banana', 'cherry'] # List of strings
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c = [1, 'hello', 3.14, True] # Mixed data types
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print(a)
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print(b)
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print(c)
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#List construction using list()
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a = list((1, 2, 3, 'apple', 4.5))
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print(a)
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b = list("GFG")
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print(b)
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a = [2] * 5
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b = [0] * 7
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print(a)
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print(b)
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a = [10, 20, 30, 40, 50]
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print(a[0])
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print(a[-1])
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print(a[1:4]) # elements from index 1 to 3
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matrix = [ [1, 2, 3],
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[4, 5, 6],
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[7, 8, 9] ]
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print(matrix[1][2])
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squares = [x**2 for x in range(1, 6)]
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print(squares)

‎Fundamentals/LoopStatements.py‎

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if i % 2 == 0:
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print("Skipping even number i =", i)
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continue
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print("Current odd i:", i)
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print("Current odd i:", i)
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list1 = [1, 2, 3]
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for item in list1:
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print("Item in list1:", item)

‎README.md‎

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- Exercises and practice problems
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- Notes on functions, loops, data structures, and OOP
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- Mini projects for hands-on learning
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- A.I and M.L using python
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It serves as both a **personal study log** and a **reference** for anyone learning Python basics.
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- Lists, Tuples, Sets, and Dictionaries
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- File Handling and Exception Management
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- Basic Object-Oriented Programming (OOP) Concepts
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- Artificial Intelligence and Machine Learning
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---
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