Part 3: AI Coding Basics
Welcome back, aspiring AI architect! Now that your development environment is set up, it's time to start building. Don't worry if you feel like you're about to juggle flaming torches while riding a unicycle – we'll take it step by step. By the end of this guide, you'll be writing AI code with the confidence of a robot doing the Macarena.
Python Basics: The Building Blocks of AI
Before we dive into the deep end of AI, let's review some Python basics. Think of these as the LEGO blocks of your AI masterpiece.
Variables and Data Types
In Python, you don't need to declare variable types. It's like a guessing game, but Python always wins.python# Integers
age = 25
# Floats
pi = 3.14159
# Strings
greeting = "Hello, AI World!"
# Booleans
is_skynet_active = False # Let's hope it stays that way
# Lists
fibonacci = [0, 1, 1, 2, 3, 5, 8]
# Dictionaries
ai_assistant = {"name": "Siri", "job": "Being sassy", "hours": 24/7}
Control Structures
These are like the traffic lights of your code, directing the flow.python# If statements
if is_skynet_active:
print("Run for the hills!")
else:
print("Keep coding, we're safe... for now.")
# For loops
for number in fibonacci:
print(f"Fibonacci number: {number}")
# While loops
countdown = 5
while countdown > 0:
print(f"T-minus {countdown}...")
countdown -= 1
print("AI has launched!")
Functions
Functions are like mini-robots that do specific tasks. They're reusable and make your code cleaner.pythondef greet_ai(ai_name):
return f"Hello, {ai_name}. Please don't take over the world."
print(greet_ai("Alexa"))
NumPy: Your Numerical Ninja
NumPy is the foundation of numerical computing in Python. It's like a calculator on steroids.pythonimport numpy as np# Create an array
arr = np.array([1, 2, 3, 4, 5])
# Basic operations
print(arr * 2) # [2 4 6 8 10]
print(np.sum(arr)) # 15
print(np.mean(arr)) # 3.0
# Create a matrix
matrix = np.array([[1, 2], [3, 4]])
print(matrix.T) # Transpose the matrix
Pandas: Data Manipulation Made Easy
Pandas is like Excel, but cooler and doesn't crash when you have too much data.pythonimport pandas as pd# Create a DataFrame
data = {
'Name': ['Siri', 'Alexa', 'Cortana'],
'Age': [10, 7, 9],
'Job': ['Apple Assistant', 'Amazon Echo', 'Microsoft Assistant']
}
df = pd.DataFrame(data)
# Basic operations
print(df.head())
print(df['Age'].mean())
print(df.sort_values('Age', ascending=False))
Matplotlib: Bringing Your Data to Life
Because sometimes, you need to see your AI's thoughts in pretty colors.pythonimport matplotlib.pyplot as plt# Create a simple line plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.title("AI Learning Progress")
plt.xlabel("Time")
plt.ylabel("Knowledge")
plt.show()
Your First AI Model: Linear Regression
Let's put it all together and create a simple AI model using scikit-learn. We'll use linear regression to predict housing prices based on square footage.pythonfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import mean_squared_errorimport numpy as npimport matplotlib.pyplot as plt# Generate some sample data
np.random.seed(42)
X = np.random.rand(100, 1) * 1000 # Square footage
y = 200 * X + 100000 + np.random.randn(100, 1) * 10000 # Price
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")
# Plot the results
plt.scatter(X_test, y_test, color='blue', label='Actual')
plt.plot(X_test, y_pred, color='red', label='Predicted')
plt.xlabel('Square Footage')
plt.ylabel('Price')
plt.title('Housing Price Prediction')
plt.legend()
plt.show()
This script creates a simple linear regression model to predict housing prices based on square footage. It generates sample data, splits it into training and testing sets, trains the model, makes predictions, and visualizes the results.
Conclusion: You're Now an AI Coder!
Congratulations! You've just written your first AI code. You've manipulated data with NumPy and Pandas, visualized it with Matplotlib, and even created a predictive model with scikit-learn. You're no longer just dipping your toes in the AI pool – you're doing backflips off the high dive! Remember, this is just the beginning. AI is a vast field with endless possibilities. Keep practicing, keep learning, and most importantly, keep your AI creations friendly. We don't want any robot uprisings on our hands! In our next part, we'll dive deeper into machine learning algorithms and start tackling more complex AI challenges. Get ready to level up your AI game! Stay curious, keep coding, and may your models always converge!