Machine Learning Algorithms: Foundations and Practical Implementations

Intermediate

Machine Learning (ML) is at the heart of most modern AI systems. It involves statistical techniques that enable computers to improve performance on tasks with experience. The primary supervised learning algorithms include linear regression, decision trees, support vector machines (SVM), and neural networks.

  • Linear Regression: Used for predicting continuous values, e.g., housing prices.
  • Decision Trees: Mimic human decision-making; useful for classification tasks.
  • Support Vector Machines: Find optimal borders between classes with maximum margin.
  • Neural Networks: Modeled after the human brain, capable of modeling complex patterns.

Below is a simple implementation of a decision tree classifier using Python's scikit-learn library:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Predict and evaluate
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2f}")

This code demonstrates how decision trees can be trained and evaluated on real datasets, forming the basis for more complex models.