Machine Learning Algorithms

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning

Supervised Learning

Supervised learning involves training models on labeled data, where the algorithm learns the relationship between input and output.

Linear Regression: Used for predicting numerical values based on relationships between variables.
Logistic Regression: A classification algorithm that predicts binary outcomes.
Decision Trees: A tree-like structure that splits data based on feature conditions.
Random Forest: An ensemble learning method combining multiple decision trees.
Support Vector Machines (SVM): Finds the best boundary between classes in high-dimensional data.

Unsupervised learning deals with unlabeled data, finding hidden patterns and structures within datasets.

K-Means Clustering: Groups data points into K clusters based on feature similarity.
Hierarchical Clustering: Creates a tree-like structure of nested clusters.
DBSCAN: A density-based clustering method that detects outliers.
Principal Component Analysis (PCA): Reduces data dimensionality while preserving variance.
Autoencoders: Neural networks designed for feature compression and reconstruction.

Reinforcement learning involves an agent interacting with an environment to maximize cumulative rewards.

Q-Learning: A value-based RL algorithm that learns optimal actions.
Deep Q-Networks (DQN): Uses neural networks to approximate Q-values.
Policy Gradient Methods: Directly optimizes the policy function for action selection.
Actor-Critic Models: Combines value-based and policy-based learning.
Monte Carlo Methods: RL techniques based on full episode rollouts.

Deep learning is a subset of ML that uses neural networks to learn hierarchical patterns in data.

Artificial Neural Networks (ANN): Basic neural networks for classification and regression.
Convolutional Neural Networks (CNN): Specially designed for image processing.
Recurrent Neural Networks (RNN): Effective for sequential data like speech and text.
Long Short-Term Memory (LSTM): An advanced RNN variant for capturing long-term dependencies.
Transformers: Used in NLP tasks like GPT and BERT.