Machine Learning Basics is an essential guide for beginners and enthusiasts who want to understand the fundamentals of machine learning. This section covers the basics of machine learning, including key concepts, algorithms, and practical applications.

Key Concepts

  • Supervised Learning: A type of machine learning where the algorithm learns from labeled training data.
  • Unsupervised Learning: A type of machine learning where the algorithm learns from unlabeled data.
  • Reinforcement Learning: A type of machine learning where the algorithm learns from interactions with an environment.

Common Algorithms

  • Linear Regression: A supervised learning algorithm used for predicting continuous values.
  • Logistic Regression: A supervised learning algorithm used for predicting binary outcomes.
  • Neural Networks: A class of algorithms that can model complex patterns in data.

Practical Applications

Machine learning is used in various fields, including:

  • Healthcare: Predicting patient outcomes and diagnosing diseases.
  • Finance: Credit scoring and fraud detection.
  • Retail: Personalized recommendations and demand forecasting.

For more information on machine learning, check out our Machine Learning Documentation.

Machine Learning

If you're interested in deep learning, don't miss our Deep Learning Basics guide!