# Machine Learning Overview Machine Learning (ML) is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience. This document serves as a central hub for this machine learning knowledge base. ## Core Concepts - [[What is Machine Learning]] - [[Types of Machine Learning]] - [[The Machine Learning Process]] - [[Key Terminology]] - [[Reading Lists]] - [[Course List]] ## Fundamental Algorithms and Techniques ### Supervised Learning - [[Site/Fundamental Algorithms and Techniques/Supervised Learning/Linear Regression|Linear Regression]] - [[Logistic Regression]] - [[Decision Trees]] - [[Random Forests]] - [[Support Vector Machines (SVM)]] - [[K-Nearest Neighbours (KNN)]] ### Unsupervised Learning - [Clustering](link-to-clustering.md) - [Dimensionality Reduction](link-to-dimensionality-reduction.md) - [Principal Component Analysis (PCA)](link-to-pca.md) - [Anomaly Detection](link-to-anomaly-detection.md) ### Semi-Supervised Learning - [Overview and Techniques](link-to-semi-supervised.md) ### Reinforcement Learning - [Overview and Key Concepts](link-to-reinforcement-learning.md) - [Q-Learning](link-to-q-learning.md) - [Policy Gradient Methods](link-to-policy-gradient.md) ## Deep Learning - [[Neural Networks Fundamentals]] - [[Convolutional Neural Networks (CNN)]] - [[Recurrent Neural Networks (RNN)]] - [[Long Short-Term Memory (LSTM)]] - [[Transformers]] - [[Generative Adversarial Networks (GAN)]] ## Advanced Topics - [[Transfer Learning]] - [[Federated Learning]] - [[Meta-Learning]] - [[Explainable AI (XAI)]] - [[AutoML]] ## Mathematics for Machine Learning - [Linear Algebra](link-to-linear-algebra.md) - [Calculus](link-to-calculus.md) - [Probability and Statistics](link-to-probability-stats.md) - [Optimization Theory](link-to-optimization.md) ## Data Preprocessing and Feature Engineering - [Data Cleaning](link-to-data-cleaning.md) - [Feature Selection](link-to-feature-selection.md) - [Feature Extraction](link-to-feature-extraction.md) - [Normalization and Standardization](link-to-normalization.md) ## Model Evaluation and Validation - [Cross-Validation](link-to-cross-validation.md) - [Metrics for Classification](link-to-classification-metrics.md) - [Metrics for Regression](link-to-regression-metrics.md) - [Overfitting and Underfitting](link-to-overfitting-underfitting.md) - [Bias-Variance Tradeoff](link-to-bias-variance.md) ## Practical Aspects - [Machine Learning Pipelines](link-to-ml-pipelines.md) - [Hyperparameter Tuning](link-to-hyperparameter-tuning.md) - [Handling Imbalanced Datasets](link-to-imbalanced-datasets.md) - [Large Scale Machine Learning](link-to-large-scale-ml.md) ## Tools and Frameworks - [Python for Machine Learning](link-to-python-ml.md) - [Scikit-learn](link-to-scikit-learn.md) - [[NumPy]] - [TensorFlow](link-to-tensorflow.md) - [PyTorch](link-to-pytorch.md) - [Keras](link-to-keras.md) - [[CUDA]] ## Applications of Machine Learning - [Computer Vision](link-to-computer-vision.md) - [Natural Language Processing](link-to-nlp.md) - [Speech Recognition](link-to-speech-recognition.md) - [Recommender Systems](link-to-recommender-systems.md) - [Time Series Analysis](link-to-time-series.md) ## Ethics and Responsible AI - [Bias and Fairness in ML](link-to-bias-fairness.md) - [Privacy and Security](link-to-privacy-security.md) - [Interpretability and Explainability](link-to-interpretability.md) - [Ethical Considerations](link-to-ethical-considerations.md) ## Future Directions and Research Areas - [Quantum Machine Learning](link-to-quantum-ml.md) - [Neuromorphic Computing](link-to-neuromorphic-computing.md) - [AI for Scientific Discovery](link-to-ai-scientific-discovery.md) - [[Embodied Artificial Intelligence]] This overview provides a comprehensive structure for exploring the vast field of machine learning. Each linked document will delve deeper into its respective topic, providing detailed explanations, mathematical foundations, algorithms, and practical implementations.