Beginner's Guide to Python & AI

Beginner's Guide to Python & AI

📌 Step 1: Basic Preparation (1-4 Weeks)

1-1. Introduction to Python (2 Weeks)

✅ Day 1-3: Variables & Data Types

Variable: A container that stores data.

age = 25  # Storing the value 25 in the variable 'age'

Data Types: Different types of values that can be stored in variables.

• int: Integer numbers (e.g., 3, 100)
• str: Strings (e.g., "Hello")
• list: Ordered collection of values (e.g., [1, "apple", True])

✅ Day 4-7: Conditional Statements & Loops

Conditional Statements (if-else): Execute different code based on conditions.

temperature = 35  # Setting the temperature variable
if temperature > 30:
    print("It's hot!")
else:
    print("It's cool!")

Loops (for): Repeating actions multiple times.

for i in range(5):  # Looping from 0 to 4
    print(i)

✅ Day 8-10: Functions & Classes

Function: A reusable block of code.

def add(a, b):
    return a + b

print(add(3, 5))  # Output: 8

Class: A blueprint for creating objects.

class Car:
    def __init__(self, brand):
        self.brand = brand

    def drive(self):
        print(f"The {self.brand} car is driving!")

my_car = Car("Hyundai")
my_car.drive()

✅ Day 11-14: Files & Libraries

Library: Pre-written code that provides extra functionality.

• Install numpy for numerical computations:

pip install numpy

• Import pandas for data analysis:

import pandas as pd

---

1-2. Setting Up Development Environment (3 Days)

✅ Essential Tools

• VSCode: A powerful text editor for coding.
  • Recommended extensions: Python, GitLens
• Git: A version control system to track code changes.

✅ Initial Setup

Virtual Environment: Creating an isolated workspace for projects.

conda create -n myenv python=3.8  # Setting up Python 3.8 environment

---

1-3. Core AI Concepts (1 Week)

✅ Machine Learning vs Deep Learning

• Machine Learning (ML): Learns patterns from data (e.g., spam email classification).
• Deep Learning (DL): Uses neural networks inspired by the human brain (e.g., image recognition).

✅ Neural Network Structure

A neural network consists of three layers:

Input Layer → Hidden Layer → Output Layer

• Input Layer: Receives data (e.g., image data).
• Hidden Layer: Processes and analyzes data.
• Output Layer: Produces final predictions (e.g., "This is a cat").

✅ Training Process

1. Data is fed into the model.
2. The model makes a prediction.
3. The prediction is compared with the actual answer to calculate the error.
4. The model is adjusted to reduce the error (backpropagation).

By following this guide, you can easily grasp the basics of Python and AI! 🚀

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Beginner's Guide to Python & AI

📌 Step 1: Basic Preparation (1-4 Weeks)

1-1. Introduction to Python (2 Weeks)

✅ Day 1-3: Variables & Data Types

Variable: A container that stores data.

age = 25  # Storing the value 25 in the variable 'age'

Data Types: Different types of values that can be stored in variables.

• int: Integer numbers (e.g., 3, 100)
• str: Strings (e.g., "Hello")
• list: Ordered collection of values (e.g., [1, "apple", True])

✅ Day 4-7: Conditional Statements & Loops

Conditional Statements (if-else): Execute different code based on conditions.

temperature = 35  # Setting the temperature variable
if temperature > 30:
    print("It's hot!")
else:
    print("It's cool!")

Loops (for): Repeating actions multiple times.

for i in range(5):  # Looping from 0 to 4
    print(i)

✅ Day 8-10: Functions & Classes

Function: A reusable block of code.

def add(a, b):
    return a + b

print(add(3, 5))  # Output: 8

Class: A blueprint for creating objects.

class Car:
    def __init__(self, brand):
        self.brand = brand

    def drive(self):
        print(f"The {self.brand} car is driving!")

my_car = Car("Hyundai")
my_car.drive()

✅ Day 11-14: Files & Libraries

Library: Pre-written code that provides extra functionality.

• Install numpy for numerical computations:

pip install numpy

• Import pandas for data analysis:

import pandas as pd

---

1-2. Setting Up Development Environment (3 Days)

✅ Essential Tools

• VSCode: A powerful text editor for coding.
  • Recommended extensions: Python, GitLens
• Git: A version control system to track code changes.

✅ Initial Setup

Virtual Environment: Creating an isolated workspace for projects.

conda create -n myenv python=3.8  # Setting up Python 3.8 environment

---

1-3. Core AI Concepts (1 Week)

✅ Machine Learning vs Deep Learning

• Machine Learning (ML): Learns patterns from data (e.g., spam email classification).
• Deep Learning (DL): Uses neural networks inspired by the human brain (e.g., image recognition).

✅ Neural Network Structure

A neural network consists of three layers:

Input Layer → Hidden Layer → Output Layer

• Input Layer: Receives data (e.g., image data).
• Hidden Layer: Processes and analyzes data.
• Output Layer: Produces final predictions (e.g., "This is a cat").

✅ Training Process

1. Data is fed into the model.
2. The model makes a prediction.
3. The prediction is compared with the actual answer to calculate the error.
4. The model is adjusted to reduce the error (backpropagation).

By following this guide, you can easily grasp the basics of Python and AI! 🚀