Lesson 13 - How to Create an AI in Python: A Beginner-Friendly Guide

Introduction

Are you curious about artificial intelligence and want to build your own AI? Python is one of the most popular languages for AI development due to its simplicity and powerful libraries. In this tutorial, you’ll learn how to create a basic AI in Python using machine learning.

Why Choose Python for AI?

Python offers several advantages for AI development:

• Easy to read and write
  
  

• Massive community support
  
  

• Powerful libraries for data science and machine learning (e.g., scikit-learn, TensorFlow, Keras)
  
  

Whether you're building a chatbot, a recommendation system, or a prediction model, Python is the go-to language.

Step 1: Install the Required Libraries

To create a simple AI model, you’ll need:

pip install numpy pandas scikit-learn

Step 2: Import the Libraries

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import accuracy_score

Step 3: Load and Prepare the Data

# Example dataset

data = {

'Weather': ['Sunny', 'Rainy', 'Sunny', 'Cloudy'],

'Temperature': ['Hot', 'Cold', 'Warm', 'Cool'],

'Play': ['No', 'Yes', 'Yes', 'Yes']

}

df = pd.DataFrame(data)

# Convert categorical data to numeric

df_encoded = pd.get_dummies(df.drop('Play', axis=1))

labels = df['Play'].map({'Yes': 1, 'No': 0})

Step 4: Train the AI Model

X_train, X_test, y_train, y_test = train_test_split(df_encoded, labels, test_size=0.25)

model = DecisionTreeClassifier()

model.fit(X_train, y_train)

Step 5: Test Your AI

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

Conclusion

That’s it! You've just built a simple AI in Python using machine learning. While this is a basic example, it forms the foundation for more complex AI projects like natural language processing, computer vision, and neural networks.