Introduction to Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience.

Types of Machine Learning

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Simple Classification Example

Here’s a basic example using scikit-learn:

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

# Sample data
X = np.array([[1, 2], [2, 3], [3, 1], [4, 4]])
y = np.array([0, 0, 1, 1])

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42
)

# Train model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# Make predictions
predictions = knn.predict(X_test)

Key Concepts

  • Feature Selection
  • Model Training
  • Cross-Validation
  • Overfitting vs Underfitting
  • Model Evaluation

Stay tuned for more machine learning tutorials!