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Machine Learning

This page contains curated notes and interesting links about machine learning, including foundational concepts, tutorials, research, and tools.

🔗 Links​

General Overviews​

Tutorials & Courses​

Research & Papers​

Tools & Libraries​

Communities & Forums​

Books​

📝 Notes​

What is Machine Learning?​

Machine learning is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing algorithms that can analyze data, detect patterns, and make decisions.

Types of Machine Learning​

  • Supervised Learning: The model learns from labeled data (e.g., classification, regression).
  • Unsupervised Learning: The model finds patterns in unlabeled data (e.g., clustering, dimensionality reduction).
  • Reinforcement Learning: The model learns by interacting with an environment and receiving feedback (rewards or penalties).

Common Algorithms​

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Neural Networks
  • K-Means Clustering
  • Principal Component Analysis (PCA)

Key Concepts​

  • Overfitting & Underfitting: Overfitting is when a model learns the training data too well, including noise. Underfitting is when a model is too simple to capture the underlying pattern.
  • Bias-Variance Tradeoff: Balancing model complexity and generalization.
  • Feature Engineering: Selecting and transforming variables to improve model performance.
  • Cross-Validation: Technique for assessing how a model generalizes to an independent dataset.
  • "Pattern Recognition and Machine Learning" by Christopher Bishop
  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron