Machine Learning Fundamentals

This reference covers fundamental machine learning concepts that form the foundation of AI systems.

Key Terminology

Supervised Learning

Learning from labeled data where the algorithm learns to map inputs to outputs based on example input-output pairs.

Unsupervised Learning

Finding patterns in data without labeled examples, such as clustering or dimensionality reduction.

Neural Networks

Computing systems inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information.

Deep Learning

A subset of machine learning using neural networks with multiple layers to learn complex patterns in data.

Common Algorithms

• Linear Regression: Predicts continuous values by finding the best line through data points
• Decision Trees: Makes decisions by splitting data based on feature values
• Random Forest: Combines multiple decision trees for improved accuracy
• Support Vector Machines: Finds optimal boundaries to separate different classes

Model Training Process

1. Data Collection: Gather relevant training data
2. Data Preprocessing: Clean and prepare data for training
3. Model Selection: Choose appropriate algorithm for the problem
4. Training: Feed data to the algorithm to learn patterns
5. Validation: Test model performance on unseen data
6. Deployment: Implement the trained model in production

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These fundamentals provide the groundwork for understanding more advanced AI concepts.