Artificial-Intelligence

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 Supervised Learning Unsupervised Learning 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! ...

Understanding AI Ethics As AI systems become more prevalent, understanding their ethical implications is crucial. Common Bias Sources Training Data Bias Algorithm Bias Interaction Bias Confirmation Bias Example: Bias in Data # Example of potential bias in data preprocessing import pandas as pd def preprocess_data(df): # Removing certain demographics might introduce bias df = df[df['age'] > 18] # Income thresholds might affect different groups differently df = df[df['income'] > 50000] return df # Better approach: Consider impact on different groups def fair_preprocess(df): # Analyze impact across demographics demographics = df.groupby('demographic_group').agg({ 'age': 'mean', 'income': 'mean' }) # Adjust thresholds based on group characteristics return df Ethical Guidelines Transparency ...